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Safety in BCI systems is paramount (Dutta, 2020; Bernal et al.,", "type": "text" } ], "index": 36 }, { "bbox": [ 105, 593, 507, 608 ], "spans": [ { "bbox": [ 105, 593, 507, 608 ], "score": 1.0, "content": "2021), because a failure would cause misdiagnoses, user frustration, or even danger while driving a", "type": "text" } ], "index": 37 }, { "bbox": [ 105, 604, 406, 618 ], "spans": [ { "bbox": [ 105, 604, 406, 618 ], "score": 1.0, "content": "wheelchair or controlling a drone, causing physical and financial damages.", "type": "text" } ], "index": 38 } ], "index": 31.5, "bbox_fs": [ 104, 461, 507, 618 ] }, { "type": "text", "bbox": [ 107, 622, 505, 732 ], "lines": [ { "bbox": [ 106, 621, 505, 634 ], "spans": [ { "bbox": [ 106, 621, 505, 634 ], "score": 1.0, "content": "Zhang & Wu (2019) were the first to show that EEG-based BCIs are vulnerable to adversarial attacks", "type": "text" } ], "index": 39 }, { "bbox": [ 105, 633, 505, 645 ], "spans": [ { "bbox": [ 105, 633, 505, 645 ], "score": 1.0, "content": "by proposing an unsupervised fast gradient sign method (FGSM) (Goodfellow et al., 2015). More", "type": "text" } ], "index": 40 }, { "bbox": [ 105, 644, 506, 657 ], "spans": [ { "bbox": [ 105, 644, 506, 657 ], "score": 1.0, "content": "recent work has proposed a more practical attack where a universal adversarial perturbation (UAP)", "type": "text" } ], "index": 41 }, { "bbox": [ 105, 654, 505, 668 ], "spans": [ { "bbox": [ 105, 654, 505, 668 ], "score": 1.0, "content": "is computed once and can be applied to all EEG trials without learning it for every new input (Liu", "type": "text" } ], "index": 42 }, { "bbox": [ 104, 664, 507, 680 ], "spans": [ { "bbox": [ 104, 664, 507, 680 ], "score": 1.0, "content": "et al., 2021). Both works assume that the acquired signals are sent to a remote compute engine, e.g.,", "type": "text" } ], "index": 43 }, { "bbox": [ 104, 676, 506, 691 ], "spans": [ { "bbox": [ 104, 676, 506, 691 ], "score": 1.0, "content": "a computer, and the attacker can alter the signals during the transmission by attaching a “jamming”", "type": "text" } ], "index": 44 }, { "bbox": [ 105, 687, 506, 702 ], "spans": [ { "bbox": [ 105, 687, 506, 702 ], "score": 1.0, "content": "module between the signal preprocessing step and the classifier. Recent developments in smart edge", "type": "text" } ], "index": 45 }, { "bbox": [ 105, 698, 507, 712 ], "spans": [ { "bbox": [ 105, 698, 507, 712 ], "score": 1.0, "content": "computing (Akmandor & Jha, 2018; Beach et al., 2021) eliminate the need for data transmission,", "type": "text" } ], "index": 46 }, { "bbox": [ 106, 710, 506, 721 ], "spans": [ { "bbox": [ 106, 710, 506, 721 ], "score": 1.0, "content": "making this attack scenario inapplicable. Novel BCI solutions (Kartsch et al., 2019; Wang et al.,", "type": "text" } ], "index": 47 }, { "bbox": [ 105, 720, 506, 733 ], "spans": [ { "bbox": [ 105, 720, 506, 733 ], "score": 1.0, "content": "2020) embed the signal processing and classification directly at the sensor edge. A more practical", "type": "text" } ], "index": 48 }, { "bbox": [ 106, 83, 505, 95 ], "spans": [ { "bbox": [ 106, 83, 505, 95 ], "score": 1.0, "content": "adversarial example has been identified by Meng et al. (2021). It consists of a square-shaped signal", "type": "text", "cross_page": true } ], "index": 0 }, { "bbox": [ 105, 93, 505, 106 ], "spans": [ { "bbox": [ 105, 93, 505, 106 ], "score": 1.0, "content": "that can be added to EEG trials before the preprocessing step. However, the attack is proposed as", "type": "text", "cross_page": true } ], "index": 1 }, { "bbox": [ 106, 105, 505, 116 ], "spans": [ { "bbox": [ 106, 105, 505, 116 ], "score": 1.0, "content": "a backdoor key, which means that the attacker has direct access to the training dataset and pollutes", "type": "text", "cross_page": true } ], "index": 2 }, { "bbox": [ 105, 115, 505, 127 ], "spans": [ { "bbox": [ 105, 115, 505, 127 ], "score": 1.0, "content": "it with adversarial examples, which is improbable if the attacker is not directly involved in the data", "type": "text", "cross_page": true } ], "index": 3 }, { "bbox": [ 105, 127, 505, 138 ], "spans": [ { "bbox": [ 105, 127, 505, 138 ], "score": 1.0, "content": "acquisition or in the training of the classifier. Li et al. (2019b) have shown an attack scenario in the", "type": "text", "cross_page": true } ], "index": 4 }, { "bbox": [ 105, 138, 505, 150 ], "spans": [ { "bbox": [ 105, 138, 505, 150 ], "score": 1.0, "content": "audio domain by considering the on-board edge processing of a wake-word detection system, where", "type": "text", "cross_page": true } ], "index": 5 }, { "bbox": [ 105, 149, 505, 160 ], "spans": [ { "bbox": [ 105, 149, 505, 160 ], "score": 1.0, "content": "an adversarial audio trace is delivered to the environment causing denial-of-service (DoS). No similar", "type": "text", "cross_page": true } ], "index": 6 }, { "bbox": [ 105, 159, 308, 171 ], "spans": [ { "bbox": [ 105, 159, 308, 171 ], "score": 1.0, "content": "studies can be currently found in the BCI domain.", "type": "text", "cross_page": true } ], "index": 7 } ], "index": 43.5, "bbox_fs": [ 104, 621, 507, 733 ] } ] }, { "preproc_blocks": [ { "type": "text", "bbox": [ 107, 82, 505, 170 ], "lines": [ { "bbox": [ 106, 83, 505, 95 ], "spans": [ { "bbox": [ 106, 83, 505, 95 ], "score": 1.0, "content": "adversarial example has been identified by Meng et al. (2021). It consists of a square-shaped signal", "type": "text" } ], "index": 0 }, { "bbox": [ 105, 93, 505, 106 ], "spans": [ { "bbox": [ 105, 93, 505, 106 ], "score": 1.0, "content": "that can be added to EEG trials before the preprocessing step. However, the attack is proposed as", "type": "text" } ], "index": 1 }, { "bbox": [ 106, 105, 505, 116 ], "spans": [ { "bbox": [ 106, 105, 505, 116 ], "score": 1.0, "content": "a backdoor key, which means that the attacker has direct access to the training dataset and pollutes", "type": "text" } ], "index": 2 }, { "bbox": [ 105, 115, 505, 127 ], "spans": [ { "bbox": [ 105, 115, 505, 127 ], "score": 1.0, "content": "it with adversarial examples, which is improbable if the attacker is not directly involved in the data", "type": "text" } ], "index": 3 }, { "bbox": [ 105, 127, 505, 138 ], "spans": [ { "bbox": [ 105, 127, 505, 138 ], "score": 1.0, "content": "acquisition or in the training of the classifier. Li et al. (2019b) have shown an attack scenario in the", "type": "text" } ], "index": 4 }, { "bbox": [ 105, 138, 505, 150 ], "spans": [ { "bbox": [ 105, 138, 505, 150 ], "score": 1.0, "content": "audio domain by considering the on-board edge processing of a wake-word detection system, where", "type": "text" } ], "index": 5 }, { "bbox": [ 105, 149, 505, 160 ], "spans": [ { "bbox": [ 105, 149, 505, 160 ], "score": 1.0, "content": "an adversarial audio trace is delivered to the environment causing denial-of-service (DoS). No similar", "type": "text" } ], "index": 6 }, { "bbox": [ 105, 159, 308, 171 ], "spans": [ { "bbox": [ 105, 159, 308, 171 ], "score": 1.0, "content": "studies can be currently found in the BCI domain.", "type": "text" } ], "index": 7 } ], "index": 3.5 }, { "type": "text", "bbox": [ 107, 176, 505, 308 ], "lines": [ { "bbox": [ 106, 175, 506, 189 ], "spans": [ { "bbox": [ 106, 175, 506, 189 ], "score": 1.0, "content": "Challenges: Designing natural attacks and modeling its propagation. Unlike in audio", "type": "text" } ], "index": 8 }, { "bbox": [ 106, 187, 506, 200 ], "spans": [ { "bbox": [ 106, 187, 506, 200 ], "score": 1.0, "content": "applications where the signal can simply propagate over-the-air and is sensed by a microphone, extra", "type": "text" } ], "index": 9 }, { "bbox": [ 106, 198, 505, 210 ], "spans": [ { "bbox": [ 106, 198, 505, 210 ], "score": 1.0, "content": "modeling is required to evaluate the signal propagation in BCIs based on the physical properties of the", "type": "text" } ], "index": 10 }, { "bbox": [ 105, 208, 506, 223 ], "spans": [ { "bbox": [ 105, 208, 506, 223 ], "score": 1.0, "content": "biological tissues. In this work, rather than assuming a “jamming” module between the preprocessing", "type": "text" } ], "index": 11 }, { "bbox": [ 106, 220, 505, 232 ], "spans": [ { "bbox": [ 106, 220, 505, 232 ], "score": 1.0, "content": "and the classification steps as in related works, we consider a more realistic and practically applicable", "type": "text" } ], "index": 12 }, { "bbox": [ 106, 231, 506, 243 ], "spans": [ { "bbox": [ 106, 231, 506, 243 ], "score": 1.0, "content": "attack scenario where the adversarial perturbations are introduced at the source of the data acquisition,", "type": "text" } ], "index": 13 }, { "bbox": [ 106, 243, 505, 254 ], "spans": [ { "bbox": [ 106, 243, 505, 254 ], "score": 1.0, "content": "as showcased in Figure 6 in Appendix A. This can be achieved, for example, via electromagnetic", "type": "text" } ], "index": 14 }, { "bbox": [ 105, 253, 505, 264 ], "spans": [ { "bbox": [ 105, 253, 505, 264 ], "score": 1.0, "content": "waves delivered to the environment (Dutta, 2020) or via transcranial current stimulation with electrical", "type": "text" } ], "index": 15 }, { "bbox": [ 105, 263, 506, 277 ], "spans": [ { "bbox": [ 105, 263, 506, 277 ], "score": 1.0, "content": "current delivered directly to the scalp (Bodranghien et al., 2017; Fertonani et al., 2015), by exploiting", "type": "text" } ], "index": 16 }, { "bbox": [ 105, 274, 506, 287 ], "spans": [ { "bbox": [ 105, 274, 506, 287 ], "score": 1.0, "content": "wearable devices, such as smart glasses or over-ear headsets (Flowneuroscience, 2021; Marin et al.,", "type": "text" } ], "index": 17 }, { "bbox": [ 106, 286, 506, 299 ], "spans": [ { "bbox": [ 106, 286, 506, 299 ], "score": 1.0, "content": "2020). The adversarial perturbations translate into electrical signals propagating over the scalp and", "type": "text" } ], "index": 18 }, { "bbox": [ 106, 297, 345, 309 ], "spans": [ { "bbox": [ 106, 297, 345, 309 ], "score": 1.0, "content": "are sensed by the electrodes in addition to the EEG signals.", "type": "text" } ], "index": 19 } ], "index": 13.5 }, { "type": "text", "bbox": [ 107, 313, 505, 390 ], "lines": [ { "bbox": [ 106, 313, 505, 326 ], "spans": [ { "bbox": [ 106, 313, 505, 326 ], "score": 1.0, "content": "To guarantee the imperceptibility of the attacks, previous works in BCIs create perturbations that", "type": "text" } ], "index": 20 }, { "bbox": [ 106, 325, 505, 336 ], "spans": [ { "bbox": [ 106, 325, 505, 336 ], "score": 1.0, "content": "are small in amplitude (Zhang & Wu, 2019; Jiang et al., 2019; Liu et al., 2021), limiting the attack", "type": "text" } ], "index": 21 }, { "bbox": [ 106, 336, 506, 348 ], "spans": [ { "bbox": [ 106, 336, 506, 348 ], "score": 1.0, "content": "success rate (ASR). Increased perturbation’s amplitude yields higher ASR (Meng et al., 2021), but", "type": "text" } ], "index": 22 }, { "bbox": [ 106, 347, 506, 359 ], "spans": [ { "bbox": [ 106, 347, 506, 359 ], "score": 1.0, "content": "makes the attack more easily detectable. Moreover, the generated perturbations are square-shaped,", "type": "text" } ], "index": 23 }, { "bbox": [ 106, 358, 505, 370 ], "spans": [ { "bbox": [ 106, 358, 505, 370 ], "score": 1.0, "content": "which is implausible for biosignals. Han et al. (2020) are the first to observe square-wave artifacts", "type": "text" } ], "index": 24 }, { "bbox": [ 105, 367, 506, 382 ], "spans": [ { "bbox": [ 105, 367, 506, 382 ], "score": 1.0, "content": "in biosignals’ attacks and propose smooth perturbations for electrocardiograms (ECGs). No similar", "type": "text" } ], "index": 25 }, { "bbox": [ 106, 380, 243, 391 ], "spans": [ { "bbox": [ 106, 380, 243, 391 ], "score": 1.0, "content": "works have been found for EEGs.", "type": "text" } ], "index": 26 } ], "index": 23 }, { "type": "text", "bbox": [ 106, 396, 505, 517 ], "lines": [ { "bbox": [ 105, 395, 505, 409 ], "spans": [ { "bbox": [ 105, 395, 505, 409 ], "score": 1.0, "content": "This work: Practical attacks on BCI models. To address the above technical challenges, and", "type": "text" } ], "index": 27 }, { "bbox": [ 106, 407, 505, 420 ], "spans": [ { "bbox": [ 106, 407, 505, 420 ], "score": 1.0, "content": "for analyzing the vulnerability of embedded BCI models in practical scenarios, we design a new", "type": "text" } ], "index": 28 }, { "bbox": [ 106, 419, 505, 431 ], "spans": [ { "bbox": [ 106, 419, 505, 431 ], "score": 1.0, "content": "attack algorithm that generates smooth adversarial examples based on the signals’ first derivative and", "type": "text" } ], "index": 29 }, { "bbox": [ 106, 430, 504, 441 ], "spans": [ { "bbox": [ 106, 430, 504, 441 ], "score": 1.0, "content": "model its propagation over the scalp based on a realistic head model by taking into consideration the", "type": "text" } ], "index": 30 }, { "bbox": [ 106, 441, 505, 453 ], "spans": [ { "bbox": [ 106, 441, 505, 453 ], "score": 1.0, "content": "attack source and the electrical and physical properties of the conducting tissues. This enables the", "type": "text" } ], "index": 31 }, { "bbox": [ 105, 451, 506, 464 ], "spans": [ { "bbox": [ 105, 451, 506, 464 ], "score": 1.0, "content": "creation of practically effective perturbations, that can be delivered by an external device to attack", "type": "text" } ], "index": 32 }, { "bbox": [ 105, 461, 506, 474 ], "spans": [ { "bbox": [ 105, 461, 506, 474 ], "score": 1.0, "content": "EEG-based BCIs at the source of signal acquisition. We attack the most energy-efficient network that", "type": "text" } ], "index": 33 }, { "bbox": [ 105, 472, 506, 486 ], "spans": [ { "bbox": [ 105, 472, 506, 486 ], "score": 1.0, "content": "has been embedded on microcontrollers for smart wearable BCIs called EEGNet (Lawhern et al.,", "type": "text" } ], "index": 34 }, { "bbox": [ 106, 484, 504, 496 ], "spans": [ { "bbox": [ 106, 484, 504, 496 ], "score": 1.0, "content": "2018; Schneider et al., 2020). It is a resource-friendly convolutional neural network (CNN) and is the", "type": "text" } ], "index": 35 }, { "bbox": [ 105, 495, 506, 509 ], "spans": [ { "bbox": [ 105, 495, 506, 509 ], "score": 1.0, "content": "SoA in terms of accuracy and energy-efficiency trade-off (Belwafi et al., 2018; Malekmohammadi", "type": "text" } ], "index": 36 }, { "bbox": [ 106, 507, 325, 518 ], "spans": [ { "bbox": [ 106, 507, 325, 518 ], "score": 1.0, "content": "et al., 2019; Wang et al., 2020; Schneider et al., 2020).", "type": "text" } ], "index": 37 } ], "index": 32 }, { "type": "text", "bbox": [ 106, 523, 506, 676 ], "lines": [ { "bbox": [ 105, 522, 506, 535 ], "spans": [ { "bbox": [ 105, 522, 506, 535 ], "score": 1.0, "content": "We evaluate our methods and show experimental results on BCIs based on the motor imagery (MI)", "type": "text" } ], "index": 38 }, { "bbox": [ 105, 534, 506, 547 ], "spans": [ { "bbox": [ 105, 534, 506, 547 ], "score": 1.0, "content": "paradigm, which is of special interest among others because it can be asynchronously self-paced", "type": "text" } ], "index": 39 }, { "bbox": [ 104, 543, 507, 559 ], "spans": [ { "bbox": [ 104, 543, 507, 559 ], "score": 1.0, "content": "without external stimuli (Freer & Yang, 2020). By imagining the movement of different body parts,", "type": "text" } ], "index": 40 }, { "bbox": [ 105, 555, 506, 569 ], "spans": [ { "bbox": [ 105, 555, 506, 569 ], "score": 1.0, "content": "the decoded intention is translated into control signals. It is widely applied in several BCI applications,", "type": "text" } ], "index": 41 }, { "bbox": [ 105, 567, 506, 579 ], "spans": [ { "bbox": [ 105, 567, 506, 579 ], "score": 1.0, "content": "such as the control of wheelchairss (Yu et al., 2018), prosthetic armss (Elstob & Secco, 2016), ground", "type": "text" } ], "index": 42 }, { "bbox": [ 105, 578, 506, 590 ], "spans": [ { "bbox": [ 105, 578, 506, 590 ], "score": 1.0, "content": "vehicles (Zhuang et al., 2021), and in communication (Brumberg et al., 2016). It has been proven", "type": "text" } ], "index": 43 }, { "bbox": [ 105, 588, 506, 601 ], "spans": [ { "bbox": [ 105, 588, 506, 601 ], "score": 1.0, "content": "to be the most difficult task to be attacked among the most common BCI paradigms (Zhang & Wu,", "type": "text" } ], "index": 44 }, { "bbox": [ 105, 599, 506, 612 ], "spans": [ { "bbox": [ 105, 599, 506, 612 ], "score": 1.0, "content": "2019; Meng et al., 2021). We evaluate our methods by “fooling” the victim model to always predict", "type": "text" } ], "index": 45 }, { "bbox": [ 105, 610, 506, 623 ], "spans": [ { "bbox": [ 105, 610, 506, 623 ], "score": 1.0, "content": "“rest” class. This essentially yields a DoS attack, because resting-state EEG signals are generally", "type": "text" } ], "index": 46 }, { "bbox": [ 105, 621, 506, 633 ], "spans": [ { "bbox": [ 105, 621, 506, 633 ], "score": 1.0, "content": "interpreted as no subject’s intention decoded, i.e., no control action needs to be taken by the BCI", "type": "text" } ], "index": 47 }, { "bbox": [ 105, 632, 506, 645 ], "spans": [ { "bbox": [ 105, 632, 506, 645 ], "score": 1.0, "content": "system (Yu et al., 2018). While for healthy subjects it might solely cause user frustration and financial", "type": "text" } ], "index": 48 }, { "bbox": [ 106, 643, 505, 655 ], "spans": [ { "bbox": [ 106, 643, 505, 655 ], "score": 1.0, "content": "losses, for severely paralyzed patients it can lead to loss of communication and independence. We", "type": "text" } ], "index": 49 }, { "bbox": [ 105, 654, 506, 667 ], "spans": [ { "bbox": [ 105, 654, 506, 667 ], "score": 1.0, "content": "generalize our methodology to an other MI task of BCI Competition IV-2a dataset and believe that it", "type": "text" } ], "index": 50 }, { "bbox": [ 105, 665, 293, 678 ], "spans": [ { "bbox": [ 105, 665, 293, 678 ], "score": 1.0, "content": "can be easily adapted to other BCI paradigms.", "type": "text" } ], "index": 51 } ], "index": 44.5 }, { "type": "text", "bbox": [ 108, 682, 306, 693 ], "lines": [ { "bbox": [ 106, 681, 307, 694 ], "spans": [ { "bbox": [ 106, 681, 307, 694 ], "score": 1.0, "content": "Main contributions. Our main contributions are:", "type": "text" } ], "index": 52 } ], "index": 52 }, { "type": "text", "bbox": [ 134, 699, 504, 722 ], "lines": [ { "bbox": [ 132, 697, 505, 712 ], "spans": [ { "bbox": [ 132, 697, 505, 712 ], "score": 1.0, "content": "• We design a new method to generate smooth adversarial perturbations that are", "type": "text" } ], "index": 53 }, { "bbox": [ 141, 709, 392, 725 ], "spans": [ { "bbox": [ 141, 709, 392, 725 ], "score": 1.0, "content": "physiologically plausible and imperceptible to the human eye.", "type": "text" } ], "index": 54 } ], "index": 53.5 } ], "page_idx": 1, "page_size": [ 612, 792 ], "discarded_blocks": [ { "type": "discarded", "bbox": [ 107, 27, 308, 37 ], "lines": [ { "bbox": [ 106, 25, 309, 39 ], "spans": [ { "bbox": [ 106, 25, 309, 39 ], "score": 1.0, "content": "Under review as a conference paper at ICLR 2022", "type": "text" } ] } ] }, { "type": "discarded", "bbox": [ 302, 752, 309, 760 ], "lines": [ { "bbox": [ 301, 750, 310, 763 ], "spans": [ { "bbox": [ 301, 750, 310, 763 ], "score": 1.0, "content": "", "type": "text", "height": 13, "width": 9 } ] } ] } ], "para_blocks": [ { "type": "text", "bbox": [ 107, 82, 505, 170 ], "lines": [], "index": 3.5, "bbox_fs": [ 105, 83, 505, 171 ], "lines_deleted": true }, { "type": "text", "bbox": [ 107, 176, 505, 308 ], "lines": [ { "bbox": [ 106, 175, 506, 189 ], "spans": [ { "bbox": [ 106, 175, 506, 189 ], "score": 1.0, "content": "Challenges: Designing natural attacks and modeling its propagation. Unlike in audio", "type": "text" } ], "index": 8 }, { "bbox": [ 106, 187, 506, 200 ], "spans": [ { "bbox": [ 106, 187, 506, 200 ], "score": 1.0, "content": "applications where the signal can simply propagate over-the-air and is sensed by a microphone, extra", "type": "text" } ], "index": 9 }, { "bbox": [ 106, 198, 505, 210 ], "spans": [ { "bbox": [ 106, 198, 505, 210 ], "score": 1.0, "content": "modeling is required to evaluate the signal propagation in BCIs based on the physical properties of the", "type": "text" } ], "index": 10 }, { "bbox": [ 105, 208, 506, 223 ], "spans": [ { "bbox": [ 105, 208, 506, 223 ], "score": 1.0, "content": "biological tissues. In this work, rather than assuming a “jamming” module between the preprocessing", "type": "text" } ], "index": 11 }, { "bbox": [ 106, 220, 505, 232 ], "spans": [ { "bbox": [ 106, 220, 505, 232 ], "score": 1.0, "content": "and the classification steps as in related works, we consider a more realistic and practically applicable", "type": "text" } ], "index": 12 }, { "bbox": [ 106, 231, 506, 243 ], "spans": [ { "bbox": [ 106, 231, 506, 243 ], "score": 1.0, "content": "attack scenario where the adversarial perturbations are introduced at the source of the data acquisition,", "type": "text" } ], "index": 13 }, { "bbox": [ 106, 243, 505, 254 ], "spans": [ { "bbox": [ 106, 243, 505, 254 ], "score": 1.0, "content": "as showcased in Figure 6 in Appendix A. This can be achieved, for example, via electromagnetic", "type": "text" } ], "index": 14 }, { "bbox": [ 105, 253, 505, 264 ], "spans": [ { "bbox": [ 105, 253, 505, 264 ], "score": 1.0, "content": "waves delivered to the environment (Dutta, 2020) or via transcranial current stimulation with electrical", "type": "text" } ], "index": 15 }, { "bbox": [ 105, 263, 506, 277 ], "spans": [ { "bbox": [ 105, 263, 506, 277 ], "score": 1.0, "content": "current delivered directly to the scalp (Bodranghien et al., 2017; Fertonani et al., 2015), by exploiting", "type": "text" } ], "index": 16 }, { "bbox": [ 105, 274, 506, 287 ], "spans": [ { "bbox": [ 105, 274, 506, 287 ], "score": 1.0, "content": "wearable devices, such as smart glasses or over-ear headsets (Flowneuroscience, 2021; Marin et al.,", "type": "text" } ], "index": 17 }, { "bbox": [ 106, 286, 506, 299 ], "spans": [ { "bbox": [ 106, 286, 506, 299 ], "score": 1.0, "content": "2020). The adversarial perturbations translate into electrical signals propagating over the scalp and", "type": "text" } ], "index": 18 }, { "bbox": [ 106, 297, 345, 309 ], "spans": [ { "bbox": [ 106, 297, 345, 309 ], "score": 1.0, "content": "are sensed by the electrodes in addition to the EEG signals.", "type": "text" } ], "index": 19 } ], "index": 13.5, "bbox_fs": [ 105, 175, 506, 309 ] }, { "type": "text", "bbox": [ 107, 313, 505, 390 ], "lines": [ { "bbox": [ 106, 313, 505, 326 ], "spans": [ { "bbox": [ 106, 313, 505, 326 ], "score": 1.0, "content": "To guarantee the imperceptibility of the attacks, previous works in BCIs create perturbations that", "type": "text" } ], "index": 20 }, { "bbox": [ 106, 325, 505, 336 ], "spans": [ { "bbox": [ 106, 325, 505, 336 ], "score": 1.0, "content": "are small in amplitude (Zhang & Wu, 2019; Jiang et al., 2019; Liu et al., 2021), limiting the attack", "type": "text" } ], "index": 21 }, { "bbox": [ 106, 336, 506, 348 ], "spans": [ { "bbox": [ 106, 336, 506, 348 ], "score": 1.0, "content": "success rate (ASR). Increased perturbation’s amplitude yields higher ASR (Meng et al., 2021), but", "type": "text" } ], "index": 22 }, { "bbox": [ 106, 347, 506, 359 ], "spans": [ { "bbox": [ 106, 347, 506, 359 ], "score": 1.0, "content": "makes the attack more easily detectable. Moreover, the generated perturbations are square-shaped,", "type": "text" } ], "index": 23 }, { "bbox": [ 106, 358, 505, 370 ], "spans": [ { "bbox": [ 106, 358, 505, 370 ], "score": 1.0, "content": "which is implausible for biosignals. Han et al. (2020) are the first to observe square-wave artifacts", "type": "text" } ], "index": 24 }, { "bbox": [ 105, 367, 506, 382 ], "spans": [ { "bbox": [ 105, 367, 506, 382 ], "score": 1.0, "content": "in biosignals’ attacks and propose smooth perturbations for electrocardiograms (ECGs). No similar", "type": "text" } ], "index": 25 }, { "bbox": [ 106, 380, 243, 391 ], "spans": [ { "bbox": [ 106, 380, 243, 391 ], "score": 1.0, "content": "works have been found for EEGs.", "type": "text" } ], "index": 26 } ], "index": 23, "bbox_fs": [ 105, 313, 506, 391 ] }, { "type": "text", "bbox": [ 106, 396, 505, 517 ], "lines": [ { "bbox": [ 105, 395, 505, 409 ], "spans": [ { "bbox": [ 105, 395, 505, 409 ], "score": 1.0, "content": "This work: Practical attacks on BCI models. To address the above technical challenges, and", "type": "text" } ], "index": 27 }, { "bbox": [ 106, 407, 505, 420 ], "spans": [ { "bbox": [ 106, 407, 505, 420 ], "score": 1.0, "content": "for analyzing the vulnerability of embedded BCI models in practical scenarios, we design a new", "type": "text" } ], "index": 28 }, { "bbox": [ 106, 419, 505, 431 ], "spans": [ { "bbox": [ 106, 419, 505, 431 ], "score": 1.0, "content": "attack algorithm that generates smooth adversarial examples based on the signals’ first derivative and", "type": "text" } ], "index": 29 }, { "bbox": [ 106, 430, 504, 441 ], "spans": [ { "bbox": [ 106, 430, 504, 441 ], "score": 1.0, "content": "model its propagation over the scalp based on a realistic head model by taking into consideration the", "type": "text" } ], "index": 30 }, { "bbox": [ 106, 441, 505, 453 ], "spans": [ { "bbox": [ 106, 441, 505, 453 ], "score": 1.0, "content": "attack source and the electrical and physical properties of the conducting tissues. This enables the", "type": "text" } ], "index": 31 }, { "bbox": [ 105, 451, 506, 464 ], "spans": [ { "bbox": [ 105, 451, 506, 464 ], "score": 1.0, "content": "creation of practically effective perturbations, that can be delivered by an external device to attack", "type": "text" } ], "index": 32 }, { "bbox": [ 105, 461, 506, 474 ], "spans": [ { "bbox": [ 105, 461, 506, 474 ], "score": 1.0, "content": "EEG-based BCIs at the source of signal acquisition. We attack the most energy-efficient network that", "type": "text" } ], "index": 33 }, { "bbox": [ 105, 472, 506, 486 ], "spans": [ { "bbox": [ 105, 472, 506, 486 ], "score": 1.0, "content": "has been embedded on microcontrollers for smart wearable BCIs called EEGNet (Lawhern et al.,", "type": "text" } ], "index": 34 }, { "bbox": [ 106, 484, 504, 496 ], "spans": [ { "bbox": [ 106, 484, 504, 496 ], "score": 1.0, "content": "2018; Schneider et al., 2020). It is a resource-friendly convolutional neural network (CNN) and is the", "type": "text" } ], "index": 35 }, { "bbox": [ 105, 495, 506, 509 ], "spans": [ { "bbox": [ 105, 495, 506, 509 ], "score": 1.0, "content": "SoA in terms of accuracy and energy-efficiency trade-off (Belwafi et al., 2018; Malekmohammadi", "type": "text" } ], "index": 36 }, { "bbox": [ 106, 507, 325, 518 ], "spans": [ { "bbox": [ 106, 507, 325, 518 ], "score": 1.0, "content": "et al., 2019; Wang et al., 2020; Schneider et al., 2020).", "type": "text" } ], "index": 37 } ], "index": 32, "bbox_fs": [ 105, 395, 506, 518 ] }, { "type": "text", "bbox": [ 106, 523, 506, 676 ], "lines": [ { "bbox": [ 105, 522, 506, 535 ], "spans": [ { "bbox": [ 105, 522, 506, 535 ], "score": 1.0, "content": "We evaluate our methods and show experimental results on BCIs based on the motor imagery (MI)", "type": "text" } ], "index": 38 }, { "bbox": [ 105, 534, 506, 547 ], "spans": [ { "bbox": [ 105, 534, 506, 547 ], "score": 1.0, "content": "paradigm, which is of special interest among others because it can be asynchronously self-paced", "type": "text" } ], "index": 39 }, { "bbox": [ 104, 543, 507, 559 ], "spans": [ { "bbox": [ 104, 543, 507, 559 ], "score": 1.0, "content": "without external stimuli (Freer & Yang, 2020). By imagining the movement of different body parts,", "type": "text" } ], "index": 40 }, { "bbox": [ 105, 555, 506, 569 ], "spans": [ { "bbox": [ 105, 555, 506, 569 ], "score": 1.0, "content": "the decoded intention is translated into control signals. It is widely applied in several BCI applications,", "type": "text" } ], "index": 41 }, { "bbox": [ 105, 567, 506, 579 ], "spans": [ { "bbox": [ 105, 567, 506, 579 ], "score": 1.0, "content": "such as the control of wheelchairss (Yu et al., 2018), prosthetic armss (Elstob & Secco, 2016), ground", "type": "text" } ], "index": 42 }, { "bbox": [ 105, 578, 506, 590 ], "spans": [ { "bbox": [ 105, 578, 506, 590 ], "score": 1.0, "content": "vehicles (Zhuang et al., 2021), and in communication (Brumberg et al., 2016). It has been proven", "type": "text" } ], "index": 43 }, { "bbox": [ 105, 588, 506, 601 ], "spans": [ { "bbox": [ 105, 588, 506, 601 ], "score": 1.0, "content": "to be the most difficult task to be attacked among the most common BCI paradigms (Zhang & Wu,", "type": "text" } ], "index": 44 }, { "bbox": [ 105, 599, 506, 612 ], "spans": [ { "bbox": [ 105, 599, 506, 612 ], "score": 1.0, "content": "2019; Meng et al., 2021). We evaluate our methods by “fooling” the victim model to always predict", "type": "text" } ], "index": 45 }, { "bbox": [ 105, 610, 506, 623 ], "spans": [ { "bbox": [ 105, 610, 506, 623 ], "score": 1.0, "content": "“rest” class. This essentially yields a DoS attack, because resting-state EEG signals are generally", "type": "text" } ], "index": 46 }, { "bbox": [ 105, 621, 506, 633 ], "spans": [ { "bbox": [ 105, 621, 506, 633 ], "score": 1.0, "content": "interpreted as no subject’s intention decoded, i.e., no control action needs to be taken by the BCI", "type": "text" } ], "index": 47 }, { "bbox": [ 105, 632, 506, 645 ], "spans": [ { "bbox": [ 105, 632, 506, 645 ], "score": 1.0, "content": "system (Yu et al., 2018). While for healthy subjects it might solely cause user frustration and financial", "type": "text" } ], "index": 48 }, { "bbox": [ 106, 643, 505, 655 ], "spans": [ { "bbox": [ 106, 643, 505, 655 ], "score": 1.0, "content": "losses, for severely paralyzed patients it can lead to loss of communication and independence. We", "type": "text" } ], "index": 49 }, { "bbox": [ 105, 654, 506, 667 ], "spans": [ { "bbox": [ 105, 654, 506, 667 ], "score": 1.0, "content": "generalize our methodology to an other MI task of BCI Competition IV-2a dataset and believe that it", "type": "text" } ], "index": 50 }, { "bbox": [ 105, 665, 293, 678 ], "spans": [ { "bbox": [ 105, 665, 293, 678 ], "score": 1.0, "content": "can be easily adapted to other BCI paradigms.", "type": "text" } ], "index": 51 } ], "index": 44.5, "bbox_fs": [ 104, 522, 507, 678 ] }, { "type": "text", "bbox": [ 108, 682, 306, 693 ], "lines": [ { "bbox": [ 106, 681, 307, 694 ], "spans": [ { "bbox": [ 106, 681, 307, 694 ], "score": 1.0, "content": "Main contributions. Our main contributions are:", "type": "text" } ], "index": 52 } ], "index": 52, "bbox_fs": [ 106, 681, 307, 694 ] }, { "type": "text", "bbox": [ 134, 699, 504, 722 ], "lines": [ { "bbox": [ 132, 697, 505, 712 ], "spans": [ { "bbox": [ 132, 697, 505, 712 ], "score": 1.0, "content": "• We design a new method to generate smooth adversarial perturbations that are", "type": "text" } ], "index": 53 }, { "bbox": [ 141, 709, 392, 725 ], "spans": [ { "bbox": [ 141, 709, 392, 725 ], "score": 1.0, "content": "physiologically plausible and imperceptible to the human eye.", "type": "text" } ], "index": 54 } ], "index": 53.5, "bbox_fs": [ 132, 697, 505, 725 ] } ] }, { "preproc_blocks": [ { "type": "text", "bbox": [ 133, 82, 505, 152 ], "lines": [ { "bbox": [ 133, 82, 506, 95 ], "spans": [ { "bbox": [ 133, 82, 506, 95 ], "score": 1.0, "content": "• We consider a practical scenario where the perturbation is added at the signal acquisition", "type": "text" } ], "index": 0 }, { "bbox": [ 140, 93, 506, 107 ], "spans": [ { "bbox": [ 140, 93, 506, 107 ], "score": 1.0, "content": "source and model its propagation constrained by the physical properties of the human scalp.", "type": "text" } ], "index": 1 }, { "bbox": [ 132, 107, 506, 120 ], "spans": [ { "bbox": [ 132, 107, 506, 120 ], "score": 1.0, "content": "• The first study of adversarial perturbations in BCI to consider the practical scenario of", "type": "text" } ], "index": 2 }, { "bbox": [ 141, 119, 506, 130 ], "spans": [ { "bbox": [ 141, 119, 506, 130 ], "score": 1.0, "content": "smart edge computing and physical signal propagation. We create both local and global", "type": "text" } ], "index": 3 }, { "bbox": [ 141, 129, 506, 141 ], "spans": [ { "bbox": [ 141, 129, 461, 141 ], "score": 1.0, "content": "perturbations and show that our attacks consistently achieve a success rate of", "type": "text" }, { "bbox": [ 462, 129, 493, 140 ], "score": 0.87, "content": "> 5 0 \\%", "type": "inline_equation" }, { "bbox": [ 493, 129, 506, 141 ], "score": 1.0, "content": "in", "type": "text" } ], "index": 4 }, { "bbox": [ 142, 141, 498, 153 ], "spans": [ { "bbox": [ 142, 141, 498, 153 ], "score": 1.0, "content": "different settings pointing to the significant vulnerability of the SoA embedded EEGNet.", "type": "text" } ], "index": 5 } ], "index": 2.5 }, { "type": "text", "bbox": [ 107, 157, 505, 180 ], "lines": [ { "bbox": [ 105, 157, 505, 169 ], "spans": [ { "bbox": [ 105, 157, 505, 169 ], "score": 1.0, "content": "We hope that our work raises awareness for potential risks and motivates the future development of", "type": "text" } ], "index": 6 }, { "bbox": [ 105, 169, 226, 180 ], "spans": [ { "bbox": [ 105, 169, 226, 180 ], "score": 1.0, "content": "appropriate countermeasures.", "type": "text" } ], "index": 7 } ], "index": 6.5 }, { "type": "title", "bbox": [ 108, 194, 201, 208 ], "lines": [ { "bbox": [ 104, 193, 201, 210 ], "spans": [ { "bbox": [ 104, 193, 201, 210 ], "score": 1.0, "content": "2 BACKGROUND", "type": "text" } ], "index": 8 } ], "index": 8 }, { "type": "title", "bbox": [ 108, 218, 241, 231 ], "lines": [ { "bbox": [ 105, 218, 241, 232 ], "spans": [ { "bbox": [ 105, 218, 241, 232 ], "score": 1.0, "content": "2.1 CLASSIFICATION IN BCIS", "type": "text" } ], "index": 9 } ], "index": 9 }, { "type": "text", "bbox": [ 106, 239, 505, 286 ], "lines": [ { "bbox": [ 105, 238, 505, 253 ], "spans": [ { "bbox": [ 105, 238, 505, 253 ], "score": 1.0, "content": "We first describe the commonly used approach in BCIs for classification, consisting of a preprocessing", "type": "text" } ], "index": 10 }, { "bbox": [ 106, 250, 505, 263 ], "spans": [ { "bbox": [ 106, 250, 449, 263 ], "score": 1.0, "content": "step and a classifier. The brain activity is recorded with an EEG device which samples", "type": "text" }, { "bbox": [ 449, 251, 467, 262 ], "score": 0.9, "content": "N _ { c h }", "type": "inline_equation" }, { "bbox": [ 467, 250, 505, 263 ], "score": 1.0, "content": "channels", "type": "text" } ], "index": 11 }, { "bbox": [ 105, 261, 506, 275 ], "spans": [ { "bbox": [ 105, 261, 133, 275 ], "score": 1.0, "content": "at rate", "type": "text" }, { "bbox": [ 134, 263, 145, 274 ], "score": 0.87, "content": "F _ { s }", "type": "inline_equation" }, { "bbox": [ 146, 261, 228, 275 ], "score": 1.0, "content": ". We define one trial", "type": "text" }, { "bbox": [ 229, 263, 235, 274 ], "score": 0.8, "content": "j", "type": "inline_equation" }, { "bbox": [ 235, 261, 247, 275 ], "score": 1.0, "content": "as", "type": "text" }, { "bbox": [ 248, 262, 295, 275 ], "score": 0.92, "content": "( \\mathbf { X } ^ { ( j ) } , y ^ { ( j ) } )", "type": "inline_equation" }, { "bbox": [ 295, 261, 326, 275 ], "score": 1.0, "content": ", where", "type": "text" }, { "bbox": [ 326, 261, 429, 275 ], "score": 0.92, "content": "y ^ { ( j ) } \\in \\{ 0 , 1 , . . . , N _ { c l } - 1 \\}", "type": "inline_equation" }, { "bbox": [ 429, 261, 506, 275 ], "score": 1.0, "content": "is the true label of", "type": "text" } ], "index": 12 }, { "bbox": [ 107, 273, 409, 288 ], "spans": [ { "bbox": [ 107, 275, 122, 286 ], "score": 0.87, "content": "N _ { c l }", "type": "inline_equation" }, { "bbox": [ 123, 273, 180, 288 ], "score": 1.0, "content": "MI tasks, and", "type": "text" }, { "bbox": [ 180, 274, 250, 285 ], "score": 0.92, "content": "\\mathbf { X } ^ { ( j ) } \\in \\mathbb { R } ^ { N _ { s } \\times N _ { c h } }", "type": "inline_equation" }, { "bbox": [ 250, 273, 409, 288 ], "score": 1.0, "content": "the multi-channel recording defined as", "type": "text" } ], "index": 13 } ], "index": 11.5 }, { "type": "interline_equation", "bbox": [ 236, 287, 374, 308 ], "lines": [ { "bbox": [ 236, 287, 374, 308 ], "spans": [ { "bbox": [ 236, 287, 374, 308 ], "score": 0.94, "content": "\\mathbf { X } ^ { ( j ) } : = \\left( \\mathbf { x } _ { 0 } ^ { ( j ) } , \\mathbf { x } _ { 1 } ^ { ( j ) } , . . . , \\mathbf { x } _ { N _ { c h } - 1 } ^ { ( j ) } \\right) ,", "type": "interline_equation", "image_path": "4adef2bd32024f0155b08bd021946820057f4e0a6d08b9b081db9e63f5cb922a.jpg" } ] } ], "index": 14, "virtual_lines": [ { "bbox": [ 236, 287, 374, 308 ], "spans": [], "index": 14 } ] }, { "type": "text", "bbox": [ 109, 312, 505, 336 ], "lines": [ { "bbox": [ 107, 309, 504, 325 ], "spans": [ { "bbox": [ 107, 309, 126, 325 ], "score": 1.0, "content": "with", "type": "text" }, { "bbox": [ 127, 309, 173, 325 ], "score": 0.93, "content": "\\mathbf { x } _ { i } ^ { ( j ) } \\in \\mathbb { R } ^ { N _ { s } }", "type": "inline_equation" }, { "bbox": [ 174, 309, 326, 325 ], "score": 1.0, "content": "corresponding to the recording of the", "type": "text" }, { "bbox": [ 327, 313, 332, 324 ], "score": 0.82, "content": "j", "type": "inline_equation" }, { "bbox": [ 333, 309, 394, 325 ], "score": 1.0, "content": "-th trial and the", "type": "text" }, { "bbox": [ 395, 313, 399, 322 ], "score": 0.78, "content": "i", "type": "inline_equation" }, { "bbox": [ 400, 309, 490, 325 ], "score": 1.0, "content": "-th channel containing", "type": "text" }, { "bbox": [ 491, 313, 504, 323 ], "score": 0.85, "content": "N _ { s }", "type": "inline_equation" } ], "index": 15 }, { "bbox": [ 107, 323, 390, 337 ], "spans": [ { "bbox": [ 107, 323, 285, 337 ], "score": 1.0, "content": "temporal samples. For simplicity, we denote", "type": "text" }, { "bbox": [ 286, 324, 330, 335 ], "score": 0.91, "content": "\\mathbf { X } : = \\mathbf { X } ^ { ( j ) }", "type": "inline_equation" }, { "bbox": [ 330, 323, 349, 337 ], "score": 1.0, "content": "and", "type": "text" }, { "bbox": [ 349, 324, 387, 336 ], "score": 0.93, "content": "y : = y ^ { ( j ) }", "type": "inline_equation" }, { "bbox": [ 387, 323, 390, 337 ], "score": 1.0, "content": ".", "type": "text" } ], "index": 16 } ], "index": 15.5 }, { "type": "text", "bbox": [ 106, 341, 503, 364 ], "lines": [ { "bbox": [ 106, 341, 505, 354 ], "spans": [ { "bbox": [ 106, 341, 505, 354 ], "score": 1.0, "content": "The EEG recordings are often preprocessed with a band-pass filter, e.g., using a Fast Fourier Transform", "type": "text" } ], "index": 17 }, { "bbox": [ 105, 351, 344, 366 ], "spans": [ { "bbox": [ 105, 351, 154, 366 ], "score": 1.0, "content": "(FFT) filter", "type": "text" }, { "bbox": [ 154, 353, 179, 365 ], "score": 0.92, "content": "h _ { b p } ( \\cdot )", "type": "inline_equation" }, { "bbox": [ 179, 351, 344, 366 ], "score": 1.0, "content": ", before being fed to a classifier, yielding", "type": "text" } ], "index": 18 } ], "index": 17.5 }, { "type": "interline_equation", "bbox": [ 191, 365, 420, 379 ], "lines": [ { "bbox": [ 191, 365, 420, 379 ], "spans": [ { "bbox": [ 191, 365, 420, 379 ], "score": 0.88, "content": "\\mathbf { X } _ { b p } = H _ { b p } ( \\mathbf { X } ) = \\left( h _ { b p } ( \\mathbf { x } _ { 0 } ) , h _ { b p } ( \\mathbf { x } _ { 1 } ) , . . . , h _ { b p } ( \\mathbf { x } _ { N _ { c h } - 1 } ) \\right) .", "type": "interline_equation", "image_path": "92578742fa44b8eb625a3c6e4e087ff7ad891f1315813fccd22129d3165cbf68.jpg" } ] } ], "index": 19, "virtual_lines": [ { "bbox": [ 191, 365, 420, 379 ], "spans": [], "index": 19 } ] }, { "type": "text", "bbox": [ 107, 380, 506, 414 ], "lines": [ { "bbox": [ 105, 378, 505, 393 ], "spans": [ { "bbox": [ 105, 378, 237, 393 ], "score": 1.0, "content": "Finally, the preprocessed signal", "type": "text" }, { "bbox": [ 238, 380, 256, 392 ], "score": 0.9, "content": "\\mathbf { X } _ { b p }", "type": "inline_equation" }, { "bbox": [ 256, 378, 401, 393 ], "score": 1.0, "content": "is classified with a trainable model", "type": "text" }, { "bbox": [ 402, 380, 409, 391 ], "score": 0.84, "content": "f", "type": "inline_equation" }, { "bbox": [ 409, 378, 482, 393 ], "score": 1.0, "content": "and is mapped to", "type": "text" }, { "bbox": [ 482, 381, 505, 392 ], "score": 0.82, "content": "\\mathbf { p } : =", "type": "inline_equation" } ], "index": 20 }, { "bbox": [ 107, 390, 506, 405 ], "spans": [ { "bbox": [ 107, 391, 142, 404 ], "score": 0.89, "content": "f \\left( { \\bf { X } } _ { b p } \\right)", "type": "inline_equation" }, { "bbox": [ 142, 390, 171, 405 ], "score": 1.0, "content": ", where", "type": "text" }, { "bbox": [ 171, 391, 210, 403 ], "score": 0.91, "content": "\\mathbf { p } \\in \\mathbb { R } ^ { N _ { c l } }", "type": "inline_equation" }, { "bbox": [ 210, 390, 506, 405 ], "score": 1.0, "content": "contains the output probabilities, e.g., originating from a softmax activation", "type": "text" } ], "index": 21 }, { "bbox": [ 105, 402, 494, 416 ], "spans": [ { "bbox": [ 105, 402, 187, 416 ], "score": 1.0, "content": "as final operation in", "type": "text" }, { "bbox": [ 188, 403, 195, 414 ], "score": 0.84, "content": "f", "type": "inline_equation" }, { "bbox": [ 195, 402, 314, 416 ], "score": 1.0, "content": ". The model’s final prediction", "type": "text" }, { "bbox": [ 315, 403, 321, 414 ], "score": 0.85, "content": "\\hat { y }", "type": "inline_equation" }, { "bbox": [ 321, 402, 482, 416 ], "score": 1.0, "content": "is the index with the maximum score in", "type": "text" }, { "bbox": [ 482, 404, 489, 414 ], "score": 0.33, "content": "\\mathbf { p }", "type": "inline_equation" }, { "bbox": [ 490, 402, 494, 416 ], "score": 1.0, "content": ":", "type": "text" } ], "index": 22 } ], "index": 21 }, { "type": "interline_equation", "bbox": [ 219, 415, 391, 439 ], "lines": [ { "bbox": [ 219, 415, 391, 439 ], "spans": [ { "bbox": [ 219, 415, 391, 439 ], "score": 0.93, "content": "\\boldsymbol { \\hat { y } } = \\boldsymbol { \\hat { f } } \\left( \\mathbf { X } _ { b p } \\right) = \\underset { y \\in \\{ 0 , \\ldots , N _ { c l } - 1 \\} } { \\mathrm { a r g m a x } } ~ f \\left( \\mathbf { X } _ { b p } \\right) [ y ] .", "type": "interline_equation", "image_path": "090de2464ceebb19449b8aaa8df74dfb9fcbf1932b76ec9c09e0616b0fd63a0c.jpg" } ] } ], "index": 23, "virtual_lines": [ { "bbox": [ 219, 415, 391, 439 ], "spans": [], "index": 23 } ] }, { "type": "title", "bbox": [ 108, 450, 248, 461 ], "lines": [ { "bbox": [ 106, 450, 249, 462 ], "spans": [ { "bbox": [ 106, 450, 249, 462 ], "score": 1.0, "content": "2.2 INSTANCE-BASED ATTACKS", "type": "text" } ], "index": 24 } ], "index": 24 }, { "type": "text", "bbox": [ 106, 470, 505, 516 ], "lines": [ { "bbox": [ 106, 470, 505, 483 ], "spans": [ { "bbox": [ 106, 470, 315, 483 ], "score": 1.0, "content": "Instance-based attacks try to fool an EEG classifier", "type": "text" }, { "bbox": [ 315, 471, 322, 482 ], "score": 0.86, "content": "f", "type": "inline_equation" }, { "bbox": [ 322, 470, 442, 483 ], "score": 1.0, "content": "to misclassify an EEG signal", "type": "text" }, { "bbox": [ 442, 471, 452, 481 ], "score": 0.58, "content": "\\mathbf { X }", "type": "inline_equation" }, { "bbox": [ 452, 470, 505, 483 ], "score": 1.0, "content": "to a targeted", "type": "text" } ], "index": 25 }, { "bbox": [ 106, 481, 505, 493 ], "spans": [ { "bbox": [ 106, 481, 128, 493 ], "score": 1.0, "content": "class", "type": "text" }, { "bbox": [ 129, 483, 138, 493 ], "score": 0.83, "content": "y _ { t }", "type": "inline_equation" }, { "bbox": [ 138, 481, 398, 493 ], "score": 1.0, "content": ". In this section, we describe the attack directly on the classifier", "type": "text" }, { "bbox": [ 398, 482, 405, 493 ], "score": 0.85, "content": "f", "type": "inline_equation" }, { "bbox": [ 406, 481, 505, 493 ], "score": 1.0, "content": "without considering the", "type": "text" } ], "index": 26 }, { "bbox": [ 105, 493, 506, 505 ], "spans": [ { "bbox": [ 105, 493, 166, 505 ], "score": 1.0, "content": "preprocessing", "type": "text" }, { "bbox": [ 166, 493, 183, 505 ], "score": 0.91, "content": "H _ { b p }", "type": "inline_equation" }, { "bbox": [ 184, 493, 506, 505 ], "score": 1.0, "content": "; the inclusion of the preprocessing is described in Section 3.3. We define an", "type": "text" } ], "index": 27 }, { "bbox": [ 104, 503, 365, 517 ], "spans": [ { "bbox": [ 104, 503, 216, 517 ], "score": 1.0, "content": "adversarial example as any", "type": "text" }, { "bbox": [ 216, 504, 324, 515 ], "score": 0.89, "content": "\\mathbf { X } ^ { * } = \\mathbf { X } + \\mathbf { V } \\in \\mathbb { R } ^ { N _ { s } \\times N _ { c h } }", "type": "inline_equation" }, { "bbox": [ 324, 503, 365, 517 ], "score": 1.0, "content": "such that", "type": "text" } ], "index": 28 } ], "index": 26.5 }, { "type": "interline_equation", "bbox": [ 259, 517, 351, 533 ], "lines": [ { "bbox": [ 259, 517, 351, 533 ], "spans": [ { "bbox": [ 259, 517, 351, 533 ], "score": 0.88, "content": "\\hat { f } \\left( \\mathbf { X } \\right) \\neq \\hat { f } \\left( \\mathbf { X } ^ { * } \\right) = y _ { t } .", "type": "interline_equation", "image_path": "ae96fbca25561b728f407743e94ef7daae4c068465648eca1989b79f54f18f8f.jpg" } ] } ], "index": 29, "virtual_lines": [ { "bbox": [ 259, 517, 351, 533 ], "spans": [], "index": 29 } ] }, { "type": "text", "bbox": [ 106, 541, 503, 563 ], "lines": [ { "bbox": [ 104, 539, 503, 554 ], "spans": [ { "bbox": [ 104, 539, 443, 554 ], "score": 1.0, "content": "FGSM. The FGSM (Goodfellow et al., 2015) generates an adversarial perturbation", "type": "text" }, { "bbox": [ 443, 540, 503, 551 ], "score": 0.9, "content": "\\mathbf { V } \\in \\mathbb { R } ^ { N _ { s } \\times N _ { c h } }", "type": "inline_equation" } ], "index": 30 }, { "bbox": [ 105, 551, 437, 565 ], "spans": [ { "bbox": [ 105, 551, 162, 565 ], "score": 1.0, "content": "of magnitude", "type": "text" }, { "bbox": [ 162, 554, 167, 562 ], "score": 0.67, "content": "\\epsilon", "type": "inline_equation" }, { "bbox": [ 168, 551, 437, 565 ], "score": 1.0, "content": "which points in the negative direction of a loss function’s gradient:", "type": "text" } ], "index": 31 } ], "index": 30.5 }, { "type": "interline_equation", "bbox": [ 239, 564, 370, 578 ], "lines": [ { "bbox": [ 239, 564, 370, 578 ], "spans": [ { "bbox": [ 239, 564, 370, 578 ], "score": 0.9, "content": "\\mathbf { V } = - \\epsilon \\cdot \\mathrm { s i g n } \\left( \\nabla _ { \\mathbf { X } } \\cdot l \\left( \\mathbf { X } , y _ { t } \\right) \\right) ,", "type": "interline_equation", "image_path": "0d4290c3afb9da2ee94681d4b078c69233401f350f53f236fd055d34090accc5.jpg" } ] } ], "index": 32, "virtual_lines": [ { "bbox": [ 239, 564, 370, 578 ], "spans": [], "index": 32 } ] }, { "type": "text", "bbox": [ 106, 578, 347, 590 ], "lines": [ { "bbox": [ 105, 577, 345, 592 ], "spans": [ { "bbox": [ 105, 577, 345, 592 ], "score": 1.0, "content": "where the loss function contains the negative log likelihood", "type": "text" } ], "index": 33 } ], "index": 33 }, { "type": "interline_equation", "bbox": [ 212, 591, 398, 604 ], "lines": [ { "bbox": [ 212, 591, 398, 604 ], "spans": [ { "bbox": [ 212, 591, 398, 604 ], "score": 0.9, "content": "l ( \\mathbf { X } , y _ { t } ) = - \\log \\left( { \\mathbf { p } } [ y _ { t } ] \\right) = - \\log \\left( f \\left( \\mathbf { X } \\right) [ y _ { t } ] \\right) .", "type": "interline_equation", "image_path": "76d960042d0c715217ff55097d0087d7852aed951636159758fb7c8f7d3fa671.jpg" } ] } ], "index": 34, "virtual_lines": [ { "bbox": [ 212, 591, 398, 604 ], "spans": [], "index": 34 } ] }, { "type": "text", "bbox": [ 105, 606, 504, 628 ], "lines": [ { "bbox": [ 105, 605, 505, 619 ], "spans": [ { "bbox": [ 105, 605, 119, 619 ], "score": 1.0, "content": "As", "type": "text" }, { "bbox": [ 119, 607, 127, 617 ], "score": 0.37, "content": "\\mathbf { p }", "type": "inline_equation" }, { "bbox": [ 128, 605, 505, 619 ], "score": 1.0, "content": "is the output of the softmax activation function, equation 6 becomes a cross-entropy loss which", "type": "text" } ], "index": 35 }, { "bbox": [ 105, 616, 382, 629 ], "spans": [ { "bbox": [ 105, 616, 194, 629 ], "score": 1.0, "content": "maximizes the output", "type": "text" }, { "bbox": [ 195, 616, 216, 628 ], "score": 0.91, "content": "\\mathbf { p } [ y _ { t } ]", "type": "inline_equation" }, { "bbox": [ 217, 616, 382, 629 ], "score": 1.0, "content": "while minimizing the remaining outputs.", "type": "text" } ], "index": 36 } ], "index": 35.5 }, { "type": "text", "bbox": [ 106, 638, 505, 694 ], "lines": [ { "bbox": [ 105, 638, 506, 652 ], "spans": [ { "bbox": [ 105, 638, 506, 652 ], "score": 1.0, "content": "PGD. The projected gradient descent (PGD) (Madry et al., 2018) is a variant of the basic iterative", "type": "text" } ], "index": 37 }, { "bbox": [ 106, 650, 506, 662 ], "spans": [ { "bbox": [ 106, 650, 506, 662 ], "score": 1.0, "content": "method (Kurakin et al.), generally considered to be more effective than FGSM. PGD aims to find a", "type": "text" } ], "index": 38 }, { "bbox": [ 105, 660, 506, 675 ], "spans": [ { "bbox": [ 105, 660, 316, 675 ], "score": 1.0, "content": "perturbation by iteratively taking small steps of size", "type": "text" }, { "bbox": [ 316, 663, 324, 671 ], "score": 0.79, "content": "\\alpha", "type": "inline_equation" }, { "bbox": [ 325, 660, 506, 675 ], "score": 1.0, "content": "in the gradient’s direction and projecting the", "type": "text" } ], "index": 39 }, { "bbox": [ 106, 672, 505, 684 ], "spans": [ { "bbox": [ 106, 672, 505, 684 ], "score": 1.0, "content": "resulting perturbation back to the sample’s neighborhood after each iteration. 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The brain activity is recorded with an EEG device which samples", "type": "text" }, { "bbox": [ 449, 251, 467, 262 ], "score": 0.9, "content": "N _ { c h }", "type": "inline_equation" }, { "bbox": [ 467, 250, 505, 263 ], "score": 1.0, "content": "channels", "type": "text" } ], "index": 11 }, { "bbox": [ 105, 261, 506, 275 ], "spans": [ { "bbox": [ 105, 261, 133, 275 ], "score": 1.0, "content": "at rate", "type": "text" }, { "bbox": [ 134, 263, 145, 274 ], "score": 0.87, "content": "F _ { s }", "type": "inline_equation" }, { "bbox": [ 146, 261, 228, 275 ], "score": 1.0, "content": ". 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For simplicity, we denote", "type": "text" }, { "bbox": [ 286, 324, 330, 335 ], "score": 0.91, "content": "\\mathbf { X } : = \\mathbf { X } ^ { ( j ) }", "type": "inline_equation" }, { "bbox": [ 330, 323, 349, 337 ], "score": 1.0, "content": "and", "type": "text" }, { "bbox": [ 349, 324, 387, 336 ], "score": 0.93, "content": "y : = y ^ { ( j ) }", "type": "inline_equation" }, { "bbox": [ 387, 323, 390, 337 ], "score": 1.0, "content": ".", "type": "text" } ], "index": 16 } ], "index": 15.5, "bbox_fs": [ 107, 309, 504, 337 ] }, { "type": "text", "bbox": [ 106, 341, 503, 364 ], "lines": [ { "bbox": [ 106, 341, 505, 354 ], "spans": [ { "bbox": [ 106, 341, 505, 354 ], "score": 1.0, "content": "The EEG recordings are often preprocessed with a band-pass filter, e.g., using a Fast Fourier Transform", "type": "text" } ], "index": 17 }, { "bbox": [ 105, 351, 344, 366 ], "spans": [ { "bbox": [ 105, 351, 154, 366 ], "score": 1.0, "content": "(FFT) filter", "type": "text" }, { "bbox": [ 154, 353, 179, 365 ], "score": 0.92, "content": "h _ { b p } ( \\cdot )", "type": "inline_equation" }, { "bbox": [ 179, 351, 344, 366 ], "score": 1.0, "content": ", before being fed to a classifier, yielding", "type": "text" } ], "index": 18 } ], "index": 17.5, "bbox_fs": [ 105, 341, 505, 366 ] }, { "type": "interline_equation", "bbox": [ 191, 365, 420, 379 ], "lines": [ { "bbox": [ 191, 365, 420, 379 ], "spans": [ { "bbox": [ 191, 365, 420, 379 ], "score": 0.88, "content": "\\mathbf { X } _ { b p } = H _ { b p } ( \\mathbf { X } ) = \\left( h _ { b p } ( \\mathbf { x } _ { 0 } ) , h _ { b p } ( \\mathbf { x } _ { 1 } ) , . . . , h _ { b p } ( \\mathbf { x } _ { N _ { c h } - 1 } ) \\right) .", "type": "interline_equation", "image_path": "92578742fa44b8eb625a3c6e4e087ff7ad891f1315813fccd22129d3165cbf68.jpg" } ] } ], "index": 19, "virtual_lines": [ { "bbox": [ 191, 365, 420, 379 ], "spans": [], "index": 19 } ] }, { "type": "text", "bbox": [ 107, 380, 506, 414 ], "lines": [ { "bbox": [ 105, 378, 505, 393 ], "spans": [ { "bbox": [ 105, 378, 237, 393 ], "score": 1.0, "content": "Finally, the preprocessed signal", "type": "text" }, { "bbox": [ 238, 380, 256, 392 ], "score": 0.9, "content": "\\mathbf { X } _ { b p }", "type": "inline_equation" }, { "bbox": [ 256, 378, 401, 393 ], "score": 1.0, "content": "is classified with a trainable model", "type": "text" }, { "bbox": [ 402, 380, 409, 391 ], "score": 0.84, "content": "f", "type": "inline_equation" }, { "bbox": [ 409, 378, 482, 393 ], "score": 1.0, "content": "and is mapped to", "type": "text" }, { "bbox": [ 482, 381, 505, 392 ], "score": 0.82, "content": "\\mathbf { p } : =", "type": "inline_equation" } ], "index": 20 }, { "bbox": [ 107, 390, 506, 405 ], "spans": [ { "bbox": [ 107, 391, 142, 404 ], "score": 0.89, "content": "f \\left( { \\bf { X } } _ { b p } \\right)", "type": "inline_equation" }, { "bbox": [ 142, 390, 171, 405 ], "score": 1.0, "content": ", where", "type": "text" }, { "bbox": [ 171, 391, 210, 403 ], "score": 0.91, "content": "\\mathbf { p } \\in \\mathbb { R } ^ { N _ { c l } }", "type": "inline_equation" }, { "bbox": [ 210, 390, 506, 405 ], "score": 1.0, "content": "contains the output probabilities, e.g., originating from a softmax activation", "type": "text" } ], "index": 21 }, { "bbox": [ 105, 402, 494, 416 ], "spans": [ { "bbox": [ 105, 402, 187, 416 ], "score": 1.0, "content": "as final operation in", "type": "text" }, { "bbox": [ 188, 403, 195, 414 ], "score": 0.84, "content": "f", "type": "inline_equation" }, { "bbox": [ 195, 402, 314, 416 ], "score": 1.0, "content": ". The model’s final prediction", "type": "text" }, { "bbox": [ 315, 403, 321, 414 ], "score": 0.85, "content": "\\hat { y }", "type": "inline_equation" }, { "bbox": [ 321, 402, 482, 416 ], "score": 1.0, "content": "is the index with the maximum score in", "type": "text" }, { "bbox": [ 482, 404, 489, 414 ], "score": 0.33, "content": "\\mathbf { p }", "type": "inline_equation" }, { "bbox": [ 490, 402, 494, 416 ], "score": 1.0, "content": ":", "type": "text" } ], "index": 22 } ], "index": 21, "bbox_fs": [ 105, 378, 506, 416 ] }, { "type": "interline_equation", "bbox": [ 219, 415, 391, 439 ], "lines": [ { "bbox": [ 219, 415, 391, 439 ], "spans": [ { "bbox": [ 219, 415, 391, 439 ], "score": 0.93, "content": "\\boldsymbol { \\hat { y } } = \\boldsymbol { \\hat { f } } \\left( \\mathbf { X } _ { b p } \\right) = \\underset { y \\in \\{ 0 , \\ldots , N _ { c l } - 1 \\} } { \\mathrm { a r g m a x } } ~ f \\left( \\mathbf { X } _ { b p } \\right) [ y ] .", "type": "interline_equation", "image_path": "090de2464ceebb19449b8aaa8df74dfb9fcbf1932b76ec9c09e0616b0fd63a0c.jpg" } ] } ], "index": 23, "virtual_lines": [ { "bbox": [ 219, 415, 391, 439 ], "spans": [], "index": 23 } ] }, { "type": "title", "bbox": [ 108, 450, 248, 461 ], "lines": [ { "bbox": [ 106, 450, 249, 462 ], "spans": [ { "bbox": [ 106, 450, 249, 462 ], "score": 1.0, "content": "2.2 INSTANCE-BASED ATTACKS", "type": "text" } ], "index": 24 } ], "index": 24 }, { "type": "text", "bbox": [ 106, 470, 505, 516 ], "lines": [ { "bbox": [ 106, 470, 505, 483 ], "spans": [ { "bbox": [ 106, 470, 315, 483 ], "score": 1.0, "content": "Instance-based attacks try to fool an EEG classifier", "type": "text" }, { "bbox": [ 315, 471, 322, 482 ], "score": 0.86, "content": "f", "type": "inline_equation" }, { "bbox": [ 322, 470, 442, 483 ], "score": 1.0, "content": "to misclassify an EEG signal", "type": "text" }, { "bbox": [ 442, 471, 452, 481 ], "score": 0.58, "content": "\\mathbf { X }", "type": "inline_equation" }, { "bbox": [ 452, 470, 505, 483 ], "score": 1.0, "content": "to a targeted", "type": "text" } ], "index": 25 }, { "bbox": [ 106, 481, 505, 493 ], "spans": [ { "bbox": [ 106, 481, 128, 493 ], "score": 1.0, "content": "class", "type": "text" }, { "bbox": [ 129, 483, 138, 493 ], "score": 0.83, "content": "y _ { t }", "type": "inline_equation" }, { "bbox": [ 138, 481, 398, 493 ], "score": 1.0, "content": ". In this section, we describe the attack directly on the classifier", "type": "text" }, { "bbox": [ 398, 482, 405, 493 ], "score": 0.85, "content": "f", "type": "inline_equation" }, { "bbox": [ 406, 481, 505, 493 ], "score": 1.0, "content": "without considering the", "type": "text" } ], "index": 26 }, { "bbox": [ 105, 493, 506, 505 ], "spans": [ { "bbox": [ 105, 493, 166, 505 ], "score": 1.0, "content": "preprocessing", "type": "text" }, { "bbox": [ 166, 493, 183, 505 ], "score": 0.91, "content": "H _ { b p }", "type": "inline_equation" }, { "bbox": [ 184, 493, 506, 505 ], "score": 1.0, "content": "; the inclusion of the preprocessing is described in Section 3.3. We define an", "type": "text" } ], "index": 27 }, { "bbox": [ 104, 503, 365, 517 ], "spans": [ { "bbox": [ 104, 503, 216, 517 ], "score": 1.0, "content": "adversarial example as any", "type": "text" }, { "bbox": [ 216, 504, 324, 515 ], "score": 0.89, "content": "\\mathbf { X } ^ { * } = \\mathbf { X } + \\mathbf { V } \\in \\mathbb { R } ^ { N _ { s } \\times N _ { c h } }", "type": "inline_equation" }, { "bbox": [ 324, 503, 365, 517 ], "score": 1.0, "content": "such that", "type": "text" } ], "index": 28 } ], "index": 26.5, "bbox_fs": [ 104, 470, 506, 517 ] }, { "type": "interline_equation", "bbox": [ 259, 517, 351, 533 ], "lines": [ { "bbox": [ 259, 517, 351, 533 ], "spans": [ { "bbox": [ 259, 517, 351, 533 ], "score": 0.88, "content": "\\hat { f } \\left( \\mathbf { X } \\right) \\neq \\hat { f } \\left( \\mathbf { X } ^ { * } \\right) = y _ { t } .", "type": "interline_equation", "image_path": "ae96fbca25561b728f407743e94ef7daae4c068465648eca1989b79f54f18f8f.jpg" } ] } ], "index": 29, "virtual_lines": [ { "bbox": [ 259, 517, 351, 533 ], "spans": [], "index": 29 } ] }, { "type": "text", "bbox": [ 106, 541, 503, 563 ], "lines": [ { "bbox": [ 104, 539, 503, 554 ], "spans": [ { "bbox": [ 104, 539, 443, 554 ], "score": 1.0, "content": "FGSM. The FGSM (Goodfellow et al., 2015) generates an adversarial perturbation", "type": "text" }, { "bbox": [ 443, 540, 503, 551 ], "score": 0.9, "content": "\\mathbf { V } \\in \\mathbb { R } ^ { N _ { s } \\times N _ { c h } }", "type": "inline_equation" } ], "index": 30 }, { "bbox": [ 105, 551, 437, 565 ], "spans": [ { "bbox": [ 105, 551, 162, 565 ], "score": 1.0, "content": "of magnitude", "type": "text" }, { "bbox": [ 162, 554, 167, 562 ], "score": 0.67, "content": "\\epsilon", "type": "inline_equation" }, { "bbox": [ 168, 551, 437, 565 ], "score": 1.0, "content": "which points in the negative direction of a loss function’s gradient:", "type": "text" } ], "index": 31 } ], "index": 30.5, "bbox_fs": [ 104, 539, 503, 565 ] }, { "type": "interline_equation", "bbox": [ 239, 564, 370, 578 ], "lines": [ { "bbox": [ 239, 564, 370, 578 ], "spans": [ { "bbox": [ 239, 564, 370, 578 ], "score": 0.9, "content": "\\mathbf { V } = - \\epsilon \\cdot \\mathrm { s i g n } \\left( \\nabla _ { \\mathbf { X } } \\cdot l \\left( \\mathbf { X } , y _ { t } \\right) \\right) ,", "type": "interline_equation", "image_path": "0d4290c3afb9da2ee94681d4b078c69233401f350f53f236fd055d34090accc5.jpg" } ] } ], "index": 32, "virtual_lines": [ { "bbox": [ 239, 564, 370, 578 ], "spans": [], "index": 32 } ] }, { "type": "text", "bbox": [ 106, 578, 347, 590 ], "lines": [ { "bbox": [ 105, 577, 345, 592 ], "spans": [ { "bbox": [ 105, 577, 345, 592 ], "score": 1.0, "content": "where the loss function contains the negative log likelihood", "type": "text" } ], "index": 33 } ], "index": 33, "bbox_fs": [ 105, 577, 345, 592 ] }, { "type": "interline_equation", "bbox": [ 212, 591, 398, 604 ], "lines": [ { "bbox": [ 212, 591, 398, 604 ], "spans": [ { "bbox": [ 212, 591, 398, 604 ], "score": 0.9, "content": "l ( \\mathbf { X } , y _ { t } ) = - \\log \\left( { \\mathbf { p } } [ y _ { t } ] \\right) = - \\log \\left( f \\left( \\mathbf { X } \\right) [ y _ { t } ] \\right) .", "type": "interline_equation", "image_path": "76d960042d0c715217ff55097d0087d7852aed951636159758fb7c8f7d3fa671.jpg" } ] } ], "index": 34, "virtual_lines": [ { "bbox": [ 212, 591, 398, 604 ], "spans": [], "index": 34 } ] }, { "type": "text", "bbox": [ 105, 606, 504, 628 ], "lines": [ { "bbox": [ 105, 605, 505, 619 ], "spans": [ { "bbox": [ 105, 605, 119, 619 ], "score": 1.0, "content": "As", "type": "text" }, { "bbox": [ 119, 607, 127, 617 ], "score": 0.37, "content": "\\mathbf { p }", "type": "inline_equation" }, { "bbox": [ 128, 605, 505, 619 ], "score": 1.0, "content": "is the output of the softmax activation function, equation 6 becomes a cross-entropy loss which", "type": "text" } ], "index": 35 }, { "bbox": [ 105, 616, 382, 629 ], "spans": [ { "bbox": [ 105, 616, 194, 629 ], "score": 1.0, "content": "maximizes the output", "type": "text" }, { "bbox": [ 195, 616, 216, 628 ], "score": 0.91, "content": "\\mathbf { p } [ y _ { t } ]", "type": "inline_equation" }, { "bbox": [ 217, 616, 382, 629 ], "score": 1.0, "content": "while minimizing the remaining outputs.", "type": "text" } ], "index": 36 } ], "index": 35.5, "bbox_fs": [ 105, 605, 505, 629 ] }, { "type": "text", "bbox": [ 106, 638, 505, 694 ], "lines": [ { "bbox": [ 105, 638, 506, 652 ], "spans": [ { "bbox": [ 105, 638, 506, 652 ], "score": 1.0, "content": "PGD. The projected gradient descent (PGD) (Madry et al., 2018) is a variant of the basic iterative", "type": "text" } ], "index": 37 }, { "bbox": [ 106, 650, 506, 662 ], "spans": [ { "bbox": [ 106, 650, 506, 662 ], "score": 1.0, "content": "method (Kurakin et al.), generally considered to be more effective than FGSM. PGD aims to find a", "type": "text" } ], "index": 38 }, { "bbox": [ 105, 660, 506, 675 ], "spans": [ { "bbox": [ 105, 660, 316, 675 ], "score": 1.0, "content": "perturbation by iteratively taking small steps of size", "type": "text" }, { "bbox": [ 316, 663, 324, 671 ], "score": 0.79, "content": "\\alpha", "type": "inline_equation" }, { "bbox": [ 325, 660, 506, 675 ], "score": 1.0, "content": "in the gradient’s direction and projecting the", "type": "text" } ], "index": 39 }, { "bbox": [ 106, 672, 505, 684 ], "spans": [ { "bbox": [ 106, 672, 505, 684 ], "score": 1.0, "content": "resulting perturbation back to the sample’s neighborhood after each iteration. We randomly initialize", "type": "text" } ], "index": 40 }, { "bbox": [ 106, 682, 472, 696 ], "spans": [ { "bbox": [ 106, 682, 188, 696 ], "score": 1.0, "content": "the attack inside the", "type": "text" }, { "bbox": [ 189, 684, 205, 694 ], "score": 0.9, "content": "L _ { \\infty }", "type": "inline_equation" }, { "bbox": [ 205, 682, 261, 696 ], "score": 1.0, "content": "ball of radius", "type": "text" }, { "bbox": [ 261, 685, 267, 693 ], "score": 0.73, "content": "\\epsilon", "type": "inline_equation" }, { "bbox": [ 267, 682, 354, 696 ], "score": 1.0, "content": "and update the attack", "type": "text" }, { "bbox": [ 355, 683, 378, 695 ], "score": 0.91, "content": "\\mathbf { V } _ { t + 1 }", "type": "inline_equation" }, { "bbox": [ 378, 682, 446, 696 ], "score": 1.0, "content": "for any iteration", "type": "text" }, { "bbox": [ 446, 684, 451, 693 ], "score": 0.76, "content": "t", "type": "inline_equation" }, { "bbox": [ 451, 682, 472, 696 ], "score": 1.0, "content": "with", "type": "text" } ], "index": 41 } ], "index": 39, "bbox_fs": [ 105, 638, 506, 696 ] }, { "type": "interline_equation", "bbox": [ 200, 696, 408, 709 ], "lines": [ { "bbox": [ 200, 696, 408, 709 ], "spans": [ { "bbox": [ 200, 696, 408, 709 ], "score": 0.83, "content": "\\mathbf { V } _ { t + 1 } = \\mathrm { c l i p } _ { \\epsilon } \\left( \\mathbf { V } _ { t } - \\alpha \\cdot \\mathrm { s i g n } \\left( \\nabla _ { \\mathbf { V } } l \\left( \\mathbf { X } + \\mathbf { V } _ { t } , y _ { t } \\right) \\right) \\right) ,", "type": "interline_equation", "image_path": "5018cbeb47f39276c0e84f65e695e567cca2809727b109293d37f372e63a064d.jpg" } ] } ], "index": 42, "virtual_lines": [ { "bbox": [ 200, 696, 408, 709 ], "spans": [], "index": 42 } ] }, { "type": "text", "bbox": [ 106, 710, 507, 732 ], "lines": [ { "bbox": [ 106, 709, 504, 721 ], "spans": [ { "bbox": [ 106, 709, 134, 721 ], "score": 1.0, "content": "where", "type": "text" }, { "bbox": [ 134, 712, 142, 720 ], "score": 0.78, "content": "\\alpha", "type": "inline_equation" }, { "bbox": [ 142, 709, 254, 721 ], "score": 1.0, "content": "is a step size smaller than", "type": "text" }, { "bbox": [ 254, 712, 260, 720 ], "score": 0.68, "content": "\\epsilon", "type": "inline_equation" }, { "bbox": [ 260, 709, 504, 721 ], "score": 1.0, "content": "which decays linearly with each iteration and the function", "type": "text" } ], "index": 43 }, { "bbox": [ 106, 720, 353, 734 ], "spans": [ { "bbox": [ 106, 720, 139, 733 ], "score": 0.87, "content": "\\mathrm { c l i p } _ { \\epsilon } \\left( \\cdot \\right)", "type": "inline_equation" }, { "bbox": [ 139, 720, 343, 734 ], "score": 1.0, "content": "clips the signal at the maximum desired amplitude", "type": "text" }, { "bbox": [ 344, 723, 349, 730 ], "score": 0.72, "content": "\\epsilon", "type": "inline_equation" }, { "bbox": [ 349, 720, 353, 734 ], "score": 1.0, "content": ".", "type": "text" } ], "index": 44 } ], "index": 43.5, "bbox_fs": [ 106, 709, 504, 734 ] } ] }, { "preproc_blocks": [ { "type": "title", "bbox": [ 108, 82, 223, 93 ], "lines": [ { "bbox": [ 106, 82, 225, 95 ], "spans": [ { "bbox": [ 106, 82, 225, 95 ], "score": 1.0, "content": "2.3 UNIVERSAL ATTACKS", "type": "text" } ], "index": 0 } ], "index": 0 }, { "type": "text", "bbox": [ 106, 102, 506, 136 ], "lines": [ { "bbox": [ 105, 101, 506, 117 ], "spans": [ { "bbox": [ 105, 101, 506, 117 ], "score": 1.0, "content": "UAPs have been introduced by Moosavi-Dezfooli et al. (2017) in the context of natural images,", "type": "text" } ], "index": 1 }, { "bbox": [ 105, 114, 506, 126 ], "spans": [ { "bbox": [ 105, 114, 506, 126 ], "score": 1.0, "content": "seeking to find an image-agnostic perturbation that fools the classifier on any input image. In the BCI", "type": "text" } ], "index": 2 }, { "bbox": [ 105, 123, 433, 138 ], "spans": [ { "bbox": [ 105, 123, 331, 138 ], "score": 1.0, "content": "domain (Liu et al., 2021), we seek to find a perturbation", "type": "text" }, { "bbox": [ 332, 124, 391, 135 ], "score": 0.91, "content": "\\mathbf { V } \\in \\mathbb { R } ^ { N _ { s } \\times N _ { c h } }", "type": "inline_equation" }, { "bbox": [ 392, 123, 433, 138 ], "score": 1.0, "content": "such that", "type": "text" } ], "index": 3 } ], "index": 2 }, { "type": "interline_equation", "bbox": [ 222, 140, 388, 155 ], "lines": [ { "bbox": [ 222, 140, 388, 155 ], "spans": [ { "bbox": [ 222, 140, 388, 155 ], "score": 0.89, "content": "\\begin{array} { r } { \\hat { f } \\left( \\mathbf { X } + \\mathbf { V } \\right) \\neq \\hat { f } \\left( \\mathbf { X } \\right) \\mathrm { f o r } ^ { * } \\mathrm { m o s t } ^ { * } \\mathbf { X } \\sim D , } \\end{array}", "type": "interline_equation", "image_path": "831387fcf3fae8922d58bc63c66078fe603ae4b2c440daa59c8a14c9c37671c9.jpg" } ] } ], "index": 4, "virtual_lines": [ { "bbox": [ 222, 140, 388, 155 ], "spans": [], "index": 4 } ] }, { "type": "text", "bbox": [ 106, 158, 507, 181 ], "lines": [ { "bbox": [ 106, 158, 505, 171 ], "spans": [ { "bbox": [ 106, 158, 132, 171 ], "score": 1.0, "content": "where", "type": "text" }, { "bbox": [ 133, 159, 142, 168 ], "score": 0.83, "content": "D", "type": "inline_equation" }, { "bbox": [ 143, 158, 505, 171 ], "score": 1.0, "content": "is the distribution of the EEG data. The UAP can be determined by optimizing the negative", "type": "text" } ], "index": 5 }, { "bbox": [ 106, 169, 495, 182 ], "spans": [ { "bbox": [ 106, 169, 244, 182 ], "score": 1.0, "content": "log-likelihood loss with respect to", "type": "text" }, { "bbox": [ 245, 169, 255, 180 ], "score": 0.69, "content": "\\mathbf { V }", "type": "inline_equation" }, { "bbox": [ 255, 169, 495, 182 ], "score": 1.0, "content": "using batch gradient descent on the trials in the training set.", "type": "text" } ], "index": 6 } ], "index": 5.5 }, { "type": "title", "bbox": [ 107, 196, 333, 209 ], "lines": [ { "bbox": [ 105, 195, 334, 210 ], "spans": [ { "bbox": [ 105, 195, 334, 210 ], "score": 1.0, "content": "3 MODELING PRACTICAL ATTACKS IN BCI", "type": "text" } ], "index": 7 } ], "index": 7 }, { "type": "text", "bbox": [ 106, 220, 506, 342 ], "lines": [ { "bbox": [ 105, 220, 507, 234 ], "spans": [ { "bbox": [ 105, 220, 507, 234 ], "score": 1.0, "content": "This section is the main contribution of the paper: we present a design of practical DoS attacks on MI-", "type": "text" } ], "index": 8 }, { "bbox": [ 105, 231, 505, 245 ], "spans": [ { "bbox": [ 105, 231, 505, 245 ], "score": 1.0, "content": "BCIs that operates at the source of the signal acquisition. We propose a new method to eliminate the", "type": "text" } ], "index": 9 }, { "bbox": [ 105, 243, 506, 255 ], "spans": [ { "bbox": [ 105, 243, 506, 255 ], "score": 1.0, "content": "square wave artifacts to generate adversarial examples that are natural and physiologically plausible.", "type": "text" } ], "index": 10 }, { "bbox": [ 105, 253, 505, 267 ], "spans": [ { "bbox": [ 105, 253, 505, 267 ], "score": 1.0, "content": "The perturbation is emitted by a smart, adversarial device placed close to the ear, e.g., a smart glass or", "type": "text" } ], "index": 11 }, { "bbox": [ 105, 265, 505, 278 ], "spans": [ { "bbox": [ 105, 265, 505, 278 ], "score": 1.0, "content": "in-ear headphones, and is propagated to the individual EEG electrodes over the scalp’s skin. As can", "type": "text" } ], "index": 12 }, { "bbox": [ 105, 276, 506, 288 ], "spans": [ { "bbox": [ 105, 276, 506, 288 ], "score": 1.0, "content": "be experimentally observed on measured EEG traces (Merlet et al., 2013; Sazgar & Young, 2019),", "type": "text" } ], "index": 13 }, { "bbox": [ 106, 287, 506, 300 ], "spans": [ { "bbox": [ 106, 287, 506, 300 ], "score": 1.0, "content": "the same electrical source, e.g., electrocardiographic activities, is sensed by each EEG electrode with", "type": "text" } ], "index": 14 }, { "bbox": [ 106, 298, 506, 311 ], "spans": [ { "bbox": [ 106, 298, 506, 311 ], "score": 1.0, "content": "different degrees of attenuation and delay. We present a practical propagation model that determines", "type": "text" } ], "index": 15 }, { "bbox": [ 106, 309, 505, 320 ], "spans": [ { "bbox": [ 106, 309, 505, 320 ], "score": 1.0, "content": "the magnitude and delay for every individual electrode based on the distance along the scalp to the", "type": "text" } ], "index": 16 }, { "bbox": [ 106, 320, 506, 333 ], "spans": [ { "bbox": [ 106, 320, 506, 333 ], "score": 1.0, "content": "adversarial device. The perturbation is trained end-to-end to fool the classifier to always output “rest,”", "type": "text" } ], "index": 17 }, { "bbox": [ 105, 330, 506, 343 ], "spans": [ { "bbox": [ 105, 330, 506, 343 ], "score": 1.0, "content": "hence DoS, while respecting the spatial model and the amplitude constraints to remain imperceptible.", "type": "text" } ], "index": 18 } ], "index": 13 }, { "type": "title", "bbox": [ 107, 354, 437, 367 ], "lines": [ { "bbox": [ 105, 354, 438, 368 ], "spans": [ { "bbox": [ 105, 354, 438, 368 ], "score": 1.0, "content": "3.1 DESIGN AND ASSESSMENT OF PHYSIOLOGICALLY PLAUSIBLE ATTACKS", "type": "text" } ], "index": 19 } ], "index": 19 }, { "type": "text", "bbox": [ 106, 375, 506, 454 ], "lines": [ { "bbox": [ 105, 375, 506, 388 ], "spans": [ { "bbox": [ 105, 375, 506, 388 ], "score": 1.0, "content": "PGD-designed attacks on EEG tend to form perturbation signals which resemble a square-wave", "type": "text" } ], "index": 20 }, { "bbox": [ 104, 386, 507, 400 ], "spans": [ { "bbox": [ 104, 386, 507, 400 ], "score": 1.0, "content": "artifact (see Figure 2), an effect that has been observed on ECG data, too (Han et al., 2020). However,", "type": "text" } ], "index": 21 }, { "bbox": [ 105, 397, 506, 410 ], "spans": [ { "bbox": [ 105, 397, 506, 410 ], "score": 1.0, "content": "EEG signals are of random nature and can be modeled as frequency dependent stationary or non-", "type": "text" } ], "index": 22 }, { "bbox": [ 105, 408, 506, 421 ], "spans": [ { "bbox": [ 105, 408, 506, 421 ], "score": 1.0, "content": "stationary random processes (Karlekar & Gupta, 2014). To this end, we introduce a new loss term in", "type": "text" } ], "index": 23 }, { "bbox": [ 106, 420, 505, 432 ], "spans": [ { "bbox": [ 106, 420, 505, 432 ], "score": 1.0, "content": "the PGD optimization such that the perturbation resembles the random nature of EEG signals, which", "type": "text" } ], "index": 24 }, { "bbox": [ 105, 430, 505, 443 ], "spans": [ { "bbox": [ 105, 430, 505, 443 ], "score": 1.0, "content": "we achieve by promoting signal changes represented in the first order derivative. We estimate the", "type": "text" } ], "index": 25 }, { "bbox": [ 103, 439, 505, 456 ], "spans": [ { "bbox": [ 103, 439, 197, 456 ], "score": 1.0, "content": "per-channel derivative", "type": "text" }, { "bbox": [ 197, 441, 365, 455 ], "score": 0.92, "content": "\\begin{array} { r } { \\bar { { \\bf V } ^ { \\prime } } = ( { \\bf v } _ { 0 } ^ { \\prime } , { \\bf v } _ { 1 } ^ { \\prime } , . . . , { \\bf v } _ { N _ { c h } - 1 } ^ { \\prime } ) \\in \\mathbb { R } ^ { N _ { s } - 1 \\times N _ { c h } } } \\end{array}", "type": "inline_equation" }, { "bbox": [ 365, 439, 505, 456 ], "score": 1.0, "content": "using the sample-wise difference:", "type": "text" } ], "index": 26 } ], "index": 23 }, { "type": "interline_equation", "bbox": [ 155, 458, 456, 471 ], "lines": [ { "bbox": [ 155, 458, 456, 471 ], "spans": [ { "bbox": [ 155, 458, 456, 471 ], "score": 0.83, "content": "\\begin{array} { r } { \\mathbf { v } _ { c } ^ { \\prime } [ t ] : = \\mathbf { v } _ { c } [ t ] - \\mathbf { v } _ { c } [ t - 1 ] \\quad t \\in \\{ 1 , 2 , . . . , N _ { s } - 1 \\} , c \\in \\{ 0 , 1 , . . . , N _ { c h } - 1 \\} } \\end{array}", "type": "interline_equation", "image_path": "ce0c71c6986adead5a7ef4f07c8039c9cf9397e84e03cd7c9175ee0546e8ce8a.jpg" } ] } ], "index": 27, "virtual_lines": [ { "bbox": [ 155, 458, 456, 471 ], "spans": [], "index": 27 } ] }, { "type": "text", "bbox": [ 106, 476, 505, 515 ], "lines": [ { "bbox": [ 105, 472, 508, 496 ], "spans": [ { "bbox": [ 105, 477, 262, 492 ], "score": 1.0, "content": "The additive loss term is determined by", "type": "text" }, { "bbox": [ 262, 475, 370, 491 ], "score": 0.91, "content": "\\begin{array} { r } { l _ { 1 } ( { \\bf V } ) = - \\frac { \\beta } { \\epsilon } \\sum _ { c = 1 } ^ { N _ { c h } } | | { \\bf v } _ { c } ^ { \\prime } | | _ { 1 } } \\end{array}", "type": "inline_equation" }, { "bbox": [ 303, 472, 508, 496 ], "score": 1.0, "content": "− β\u000f PNchc=1 ||v0c||1, where || · ||1 is the `1-norm, \u000f the", "type": "text" } ], "index": 28 }, { "bbox": [ 106, 489, 505, 502 ], "spans": [ { "bbox": [ 106, 489, 260, 502 ], "score": 1.0, "content": "maximum perturbation amplitude, and", "type": "text" }, { "bbox": [ 261, 490, 286, 500 ], "score": 0.9, "content": "\\beta \\geq 0", "type": "inline_equation" }, { "bbox": [ 286, 489, 505, 502 ], "score": 1.0, "content": "a weighting factor. When designing a one-dimensional", "type": "text" } ], "index": 29 }, { "bbox": [ 105, 500, 347, 515 ], "spans": [ { "bbox": [ 105, 500, 271, 515 ], "score": 1.0, "content": "perturbation, the derivative loss becomes", "type": "text" }, { "bbox": [ 272, 500, 343, 515 ], "score": 0.94, "content": "\\begin{array} { r } { l _ { 1 } ( \\mathbf { v } ) = - \\frac { \\beta } { \\epsilon } | | \\mathbf { v } ^ { \\prime } | | } \\end{array}", "type": "inline_equation" }, { "bbox": [ 343, 500, 347, 515 ], "score": 1.0, "content": ".", "type": "text" } ], "index": 30 } ], "index": 29 }, { "type": "text", "bbox": [ 107, 524, 505, 580 ], "lines": [ { "bbox": [ 105, 524, 505, 538 ], "spans": [ { "bbox": [ 105, 524, 505, 538 ], "score": 1.0, "content": "Measuring the Plausibility of Attacks None of the previous works have given quantitative", "type": "text" } ], "index": 31 }, { "bbox": [ 105, 536, 505, 548 ], "spans": [ { "bbox": [ 105, 536, 505, 548 ], "score": 1.0, "content": "measures to assess the physiological plausibility of an EEG adversarial attack. In this work, we", "type": "text" } ], "index": 32 }, { "bbox": [ 105, 547, 505, 559 ], "spans": [ { "bbox": [ 105, 547, 505, 559 ], "score": 1.0, "content": "propose data-driven measures for quantifying the naturalism of an attack. We compute either the", "type": "text" } ], "index": 33 }, { "bbox": [ 106, 558, 504, 570 ], "spans": [ { "bbox": [ 106, 558, 504, 570 ], "score": 1.0, "content": "cross correlation, the Euclidian distance, or the cosine similarity between the attacked signal and the", "type": "text" } ], "index": 34 }, { "bbox": [ 106, 569, 491, 581 ], "spans": [ { "bbox": [ 106, 569, 291, 581 ], "score": 1.0, "content": "original EEG, and average the values over the", "type": "text" }, { "bbox": [ 291, 569, 309, 580 ], "score": 0.91, "content": "N _ { c h }", "type": "inline_equation" }, { "bbox": [ 309, 569, 491, 581 ], "score": 1.0, "content": "channels and over the samples in the dataset.", "type": "text" } ], "index": 35 } ], "index": 33 }, { "type": "title", "bbox": [ 108, 593, 262, 604 ], "lines": [ { "bbox": [ 106, 593, 264, 606 ], "spans": [ { "bbox": [ 106, 593, 264, 606 ], "score": 1.0, "content": "3.2 SPATIAL PROPAGATION MODEL", "type": "text" } ], "index": 36 } ], "index": 36 }, { "type": "text", "bbox": [ 106, 613, 505, 713 ], "lines": [ { "bbox": [ 106, 613, 506, 627 ], "spans": [ { "bbox": [ 106, 613, 506, 627 ], "score": 1.0, "content": "So far, a perturbation signal was designed for every individual channel. It is unrealistic for an attacker", "type": "text" } ], "index": 37 }, { "bbox": [ 105, 624, 505, 637 ], "spans": [ { "bbox": [ 105, 624, 505, 637 ], "score": 1.0, "content": "to perturb the signal for all individual channels simultaneously; hence, we consider a more practical", "type": "text" } ], "index": 38 }, { "bbox": [ 104, 633, 506, 649 ], "spans": [ { "bbox": [ 104, 633, 256, 649 ], "score": 1.0, "content": "use case where the perturbation signal", "type": "text" }, { "bbox": [ 257, 635, 293, 646 ], "score": 0.91, "content": "\\mathbf { v } \\in \\mathbb { R } ^ { N _ { s } }", "type": "inline_equation" }, { "bbox": [ 293, 633, 506, 649 ], "score": 1.0, "content": "is emitted from one location, e.g., from an adversarial", "type": "text" } ], "index": 39 }, { "bbox": [ 105, 645, 505, 659 ], "spans": [ { "bbox": [ 105, 645, 505, 659 ], "score": 1.0, "content": "device placed on the left side of the subject or close to the left ear. More specifically, in this study, we", "type": "text" } ], "index": 40 }, { "bbox": [ 105, 658, 506, 671 ], "spans": [ { "bbox": [ 105, 658, 506, 671 ], "score": 1.0, "content": "assume that the EEG electrode at the position T9 according to the international 10-10 system (Sch),", "type": "text" } ], "index": 41 }, { "bbox": [ 105, 667, 505, 683 ], "spans": [ { "bbox": [ 105, 667, 505, 683 ], "score": 1.0, "content": "which is the closest to the left ear, senses the largest perturbation. The signal subsequently propagates", "type": "text" } ], "index": 42 }, { "bbox": [ 105, 678, 505, 693 ], "spans": [ { "bbox": [ 105, 678, 505, 693 ], "score": 1.0, "content": "over the skin to each electrode, which results in an individual magnitude and delay depending on", "type": "text" } ], "index": 43 }, { "bbox": [ 106, 690, 506, 703 ], "spans": [ { "bbox": [ 106, 690, 506, 703 ], "score": 1.0, "content": "the distance between the adversarial device and the electrode. More formally, we model the sensed", "type": "text" } ], "index": 44 }, { "bbox": [ 105, 702, 290, 713 ], "spans": [ { "bbox": [ 105, 702, 201, 713 ], "score": 1.0, "content": "perturbation at channel", "type": "text" }, { "bbox": [ 201, 702, 205, 711 ], "score": 0.75, "content": "i", "type": "inline_equation" }, { "bbox": [ 206, 702, 273, 713 ], "score": 1.0, "content": "and time instant", "type": "text" }, { "bbox": [ 273, 703, 278, 711 ], "score": 0.8, "content": "t", "type": "inline_equation" }, { "bbox": [ 278, 702, 290, 713 ], "score": 1.0, "content": "as", "type": "text" } ], "index": 45 } ], "index": 41 }, { "type": "interline_equation", "bbox": [ 204, 716, 406, 730 ], "lines": [ { "bbox": [ 204, 716, 406, 730 ], "spans": [ { "bbox": [ 204, 716, 406, 730 ], "score": 0.88, "content": "h _ { i } ( \\mathbf { v } , \\lambda _ { m } , \\lambda _ { d } ) ( t ) : = m ( l _ { i } , \\lambda _ { m } ) \\cdot \\mathbf { v } \\left( t - d ( l _ { i } , \\lambda _ { d } ) \\right) ,", "type": "interline_equation", "image_path": "376c0312852c9f41d7a3778ae583d8e40b0cf5932a3541026cd26602e02373b7.jpg" } ] } ], "index": 46, "virtual_lines": [ { "bbox": [ 204, 716, 406, 730 ], "spans": [], "index": 46 } ] } ], "page_idx": 3, "page_size": [ 612, 792 ], "discarded_blocks": [ { "type": "discarded", "bbox": [ 106, 26, 308, 37 ], "lines": [ { "bbox": [ 106, 25, 309, 38 ], "spans": [ { "bbox": [ 106, 25, 309, 38 ], "score": 1.0, "content": "Under review as a conference paper at ICLR 2022", "type": "text" } ] } ] }, { "type": "discarded", "bbox": [ 302, 752, 308, 759 ], "lines": [] } ], "para_blocks": [ { "type": "title", "bbox": [ 108, 82, 223, 93 ], "lines": [ { "bbox": [ 106, 82, 225, 95 ], "spans": [ { "bbox": [ 106, 82, 225, 95 ], "score": 1.0, "content": "2.3 UNIVERSAL ATTACKS", "type": "text" } ], "index": 0 } ], "index": 0 }, { "type": "text", "bbox": [ 106, 102, 506, 136 ], "lines": [ { "bbox": [ 105, 101, 506, 117 ], "spans": [ { "bbox": [ 105, 101, 506, 117 ], "score": 1.0, "content": "UAPs have been introduced by Moosavi-Dezfooli et al. (2017) in the context of natural images,", "type": "text" } ], "index": 1 }, { "bbox": [ 105, 114, 506, 126 ], "spans": [ { "bbox": [ 105, 114, 506, 126 ], "score": 1.0, "content": "seeking to find an image-agnostic perturbation that fools the classifier on any input image. In the BCI", "type": "text" } ], "index": 2 }, { "bbox": [ 105, 123, 433, 138 ], "spans": [ { "bbox": [ 105, 123, 331, 138 ], "score": 1.0, "content": "domain (Liu et al., 2021), we seek to find a perturbation", "type": "text" }, { "bbox": [ 332, 124, 391, 135 ], "score": 0.91, "content": "\\mathbf { V } \\in \\mathbb { R } ^ { N _ { s } \\times N _ { c h } }", "type": "inline_equation" }, { "bbox": [ 392, 123, 433, 138 ], "score": 1.0, "content": "such that", "type": "text" } ], "index": 3 } ], "index": 2, "bbox_fs": [ 105, 101, 506, 138 ] }, { "type": "interline_equation", "bbox": [ 222, 140, 388, 155 ], "lines": [ { "bbox": [ 222, 140, 388, 155 ], "spans": [ { "bbox": [ 222, 140, 388, 155 ], "score": 0.89, "content": "\\begin{array} { r } { \\hat { f } \\left( \\mathbf { X } + \\mathbf { V } \\right) \\neq \\hat { f } \\left( \\mathbf { X } \\right) \\mathrm { f o r } ^ { * } \\mathrm { m o s t } ^ { * } \\mathbf { X } \\sim D , } \\end{array}", "type": "interline_equation", "image_path": "831387fcf3fae8922d58bc63c66078fe603ae4b2c440daa59c8a14c9c37671c9.jpg" } ] } ], "index": 4, "virtual_lines": [ { "bbox": [ 222, 140, 388, 155 ], "spans": [], "index": 4 } ] }, { "type": "text", "bbox": [ 106, 158, 507, 181 ], "lines": [ { "bbox": [ 106, 158, 505, 171 ], "spans": [ { "bbox": [ 106, 158, 132, 171 ], "score": 1.0, "content": "where", "type": "text" }, { "bbox": [ 133, 159, 142, 168 ], "score": 0.83, "content": "D", "type": "inline_equation" }, { "bbox": [ 143, 158, 505, 171 ], "score": 1.0, "content": "is the distribution of the EEG data. The UAP can be determined by optimizing the negative", "type": "text" } ], "index": 5 }, { "bbox": [ 106, 169, 495, 182 ], "spans": [ { "bbox": [ 106, 169, 244, 182 ], "score": 1.0, "content": "log-likelihood loss with respect to", "type": "text" }, { "bbox": [ 245, 169, 255, 180 ], "score": 0.69, "content": "\\mathbf { V }", "type": "inline_equation" }, { "bbox": [ 255, 169, 495, 182 ], "score": 1.0, "content": "using batch gradient descent on the trials in the training set.", "type": "text" } ], "index": 6 } ], "index": 5.5, "bbox_fs": [ 106, 158, 505, 182 ] }, { "type": "title", "bbox": [ 107, 196, 333, 209 ], "lines": [ { "bbox": [ 105, 195, 334, 210 ], "spans": [ { "bbox": [ 105, 195, 334, 210 ], "score": 1.0, "content": "3 MODELING PRACTICAL ATTACKS IN BCI", "type": "text" } ], "index": 7 } ], "index": 7 }, { "type": "text", "bbox": [ 106, 220, 506, 342 ], "lines": [ { "bbox": [ 105, 220, 507, 234 ], "spans": [ { "bbox": [ 105, 220, 507, 234 ], "score": 1.0, "content": "This section is the main contribution of the paper: we present a design of practical DoS attacks on MI-", "type": "text" } ], "index": 8 }, { "bbox": [ 105, 231, 505, 245 ], "spans": [ { "bbox": [ 105, 231, 505, 245 ], "score": 1.0, "content": "BCIs that operates at the source of the signal acquisition. We propose a new method to eliminate the", "type": "text" } ], "index": 9 }, { "bbox": [ 105, 243, 506, 255 ], "spans": [ { "bbox": [ 105, 243, 506, 255 ], "score": 1.0, "content": "square wave artifacts to generate adversarial examples that are natural and physiologically plausible.", "type": "text" } ], "index": 10 }, { "bbox": [ 105, 253, 505, 267 ], "spans": [ { "bbox": [ 105, 253, 505, 267 ], "score": 1.0, "content": "The perturbation is emitted by a smart, adversarial device placed close to the ear, e.g., a smart glass or", "type": "text" } ], "index": 11 }, { "bbox": [ 105, 265, 505, 278 ], "spans": [ { "bbox": [ 105, 265, 505, 278 ], "score": 1.0, "content": "in-ear headphones, and is propagated to the individual EEG electrodes over the scalp’s skin. As can", "type": "text" } ], "index": 12 }, { "bbox": [ 105, 276, 506, 288 ], "spans": [ { "bbox": [ 105, 276, 506, 288 ], "score": 1.0, "content": "be experimentally observed on measured EEG traces (Merlet et al., 2013; Sazgar & Young, 2019),", "type": "text" } ], "index": 13 }, { "bbox": [ 106, 287, 506, 300 ], "spans": [ { "bbox": [ 106, 287, 506, 300 ], "score": 1.0, "content": "the same electrical source, e.g., electrocardiographic activities, is sensed by each EEG electrode with", "type": "text" } ], "index": 14 }, { "bbox": [ 106, 298, 506, 311 ], "spans": [ { "bbox": [ 106, 298, 506, 311 ], "score": 1.0, "content": "different degrees of attenuation and delay. We present a practical propagation model that determines", "type": "text" } ], "index": 15 }, { "bbox": [ 106, 309, 505, 320 ], "spans": [ { "bbox": [ 106, 309, 505, 320 ], "score": 1.0, "content": "the magnitude and delay for every individual electrode based on the distance along the scalp to the", "type": "text" } ], "index": 16 }, { "bbox": [ 106, 320, 506, 333 ], "spans": [ { "bbox": [ 106, 320, 506, 333 ], "score": 1.0, "content": "adversarial device. The perturbation is trained end-to-end to fool the classifier to always output “rest,”", "type": "text" } ], "index": 17 }, { "bbox": [ 105, 330, 506, 343 ], "spans": [ { "bbox": [ 105, 330, 506, 343 ], "score": 1.0, "content": "hence DoS, while respecting the spatial model and the amplitude constraints to remain imperceptible.", "type": "text" } ], "index": 18 } ], "index": 13, "bbox_fs": [ 105, 220, 507, 343 ] }, { "type": "title", "bbox": [ 107, 354, 437, 367 ], "lines": [ { "bbox": [ 105, 354, 438, 368 ], "spans": [ { "bbox": [ 105, 354, 438, 368 ], "score": 1.0, "content": "3.1 DESIGN AND ASSESSMENT OF PHYSIOLOGICALLY PLAUSIBLE ATTACKS", "type": "text" } ], "index": 19 } ], "index": 19 }, { "type": "text", "bbox": [ 106, 375, 506, 454 ], "lines": [ { "bbox": [ 105, 375, 506, 388 ], "spans": [ { "bbox": [ 105, 375, 506, 388 ], "score": 1.0, "content": "PGD-designed attacks on EEG tend to form perturbation signals which resemble a square-wave", "type": "text" } ], "index": 20 }, { "bbox": [ 104, 386, 507, 400 ], "spans": [ { "bbox": [ 104, 386, 507, 400 ], "score": 1.0, "content": "artifact (see Figure 2), an effect that has been observed on ECG data, too (Han et al., 2020). However,", "type": "text" } ], "index": 21 }, { "bbox": [ 105, 397, 506, 410 ], "spans": [ { "bbox": [ 105, 397, 506, 410 ], "score": 1.0, "content": "EEG signals are of random nature and can be modeled as frequency dependent stationary or non-", "type": "text" } ], "index": 22 }, { "bbox": [ 105, 408, 506, 421 ], "spans": [ { "bbox": [ 105, 408, 506, 421 ], "score": 1.0, "content": "stationary random processes (Karlekar & Gupta, 2014). To this end, we introduce a new loss term in", "type": "text" } ], "index": 23 }, { "bbox": [ 106, 420, 505, 432 ], "spans": [ { "bbox": [ 106, 420, 505, 432 ], "score": 1.0, "content": "the PGD optimization such that the perturbation resembles the random nature of EEG signals, which", "type": "text" } ], "index": 24 }, { "bbox": [ 105, 430, 505, 443 ], "spans": [ { "bbox": [ 105, 430, 505, 443 ], "score": 1.0, "content": "we achieve by promoting signal changes represented in the first order derivative. We estimate the", "type": "text" } ], "index": 25 }, { "bbox": [ 103, 439, 505, 456 ], "spans": [ { "bbox": [ 103, 439, 197, 456 ], "score": 1.0, "content": "per-channel derivative", "type": "text" }, { "bbox": [ 197, 441, 365, 455 ], "score": 0.92, "content": "\\begin{array} { r } { \\bar { { \\bf V } ^ { \\prime } } = ( { \\bf v } _ { 0 } ^ { \\prime } , { \\bf v } _ { 1 } ^ { \\prime } , . . . , { \\bf v } _ { N _ { c h } - 1 } ^ { \\prime } ) \\in \\mathbb { R } ^ { N _ { s } - 1 \\times N _ { c h } } } \\end{array}", "type": "inline_equation" }, { "bbox": [ 365, 439, 505, 456 ], "score": 1.0, "content": "using the sample-wise difference:", "type": "text" } ], "index": 26 } ], "index": 23, "bbox_fs": [ 103, 375, 507, 456 ] }, { "type": "interline_equation", "bbox": [ 155, 458, 456, 471 ], "lines": [ { "bbox": [ 155, 458, 456, 471 ], "spans": [ { "bbox": [ 155, 458, 456, 471 ], "score": 0.83, "content": "\\begin{array} { r } { \\mathbf { v } _ { c } ^ { \\prime } [ t ] : = \\mathbf { v } _ { c } [ t ] - \\mathbf { v } _ { c } [ t - 1 ] \\quad t \\in \\{ 1 , 2 , . . . , N _ { s } - 1 \\} , c \\in \\{ 0 , 1 , . . . , N _ { c h } - 1 \\} } \\end{array}", "type": "interline_equation", "image_path": "ce0c71c6986adead5a7ef4f07c8039c9cf9397e84e03cd7c9175ee0546e8ce8a.jpg" } ] } ], "index": 27, "virtual_lines": [ { "bbox": [ 155, 458, 456, 471 ], "spans": [], "index": 27 } ] }, { "type": "text", "bbox": [ 106, 476, 505, 515 ], "lines": [ { "bbox": [ 105, 472, 508, 496 ], "spans": [ { "bbox": [ 105, 477, 262, 492 ], "score": 1.0, "content": "The additive loss term is determined by", "type": "text" }, { "bbox": [ 262, 475, 370, 491 ], "score": 0.91, "content": "\\begin{array} { r } { l _ { 1 } ( { \\bf V } ) = - \\frac { \\beta } { \\epsilon } \\sum _ { c = 1 } ^ { N _ { c h } } | | { \\bf v } _ { c } ^ { \\prime } | | _ { 1 } } \\end{array}", "type": "inline_equation" }, { "bbox": [ 303, 472, 508, 496 ], "score": 1.0, "content": "− β\u000f PNchc=1 ||v0c||1, where || · ||1 is the `1-norm, \u000f the", "type": "text" } ], "index": 28 }, { "bbox": [ 106, 489, 505, 502 ], "spans": [ { "bbox": [ 106, 489, 260, 502 ], "score": 1.0, "content": "maximum perturbation amplitude, and", "type": "text" }, { "bbox": [ 261, 490, 286, 500 ], "score": 0.9, "content": "\\beta \\geq 0", "type": "inline_equation" }, { "bbox": [ 286, 489, 505, 502 ], "score": 1.0, "content": "a weighting factor. When designing a one-dimensional", "type": "text" } ], "index": 29 }, { "bbox": [ 105, 500, 347, 515 ], "spans": [ { "bbox": [ 105, 500, 271, 515 ], "score": 1.0, "content": "perturbation, the derivative loss becomes", "type": "text" }, { "bbox": [ 272, 500, 343, 515 ], "score": 0.94, "content": "\\begin{array} { r } { l _ { 1 } ( \\mathbf { v } ) = - \\frac { \\beta } { \\epsilon } | | \\mathbf { v } ^ { \\prime } | | } \\end{array}", "type": "inline_equation" }, { "bbox": [ 343, 500, 347, 515 ], "score": 1.0, "content": ".", "type": "text" } ], "index": 30 } ], "index": 29, "bbox_fs": [ 105, 472, 508, 515 ] }, { "type": "text", "bbox": [ 107, 524, 505, 580 ], "lines": [ { "bbox": [ 105, 524, 505, 538 ], "spans": [ { "bbox": [ 105, 524, 505, 538 ], "score": 1.0, "content": "Measuring the Plausibility of Attacks None of the previous works have given quantitative", "type": "text" } ], "index": 31 }, { "bbox": [ 105, 536, 505, 548 ], "spans": [ { "bbox": [ 105, 536, 505, 548 ], "score": 1.0, "content": "measures to assess the physiological plausibility of an EEG adversarial attack. In this work, we", "type": "text" } ], "index": 32 }, { "bbox": [ 105, 547, 505, 559 ], "spans": [ { "bbox": [ 105, 547, 505, 559 ], "score": 1.0, "content": "propose data-driven measures for quantifying the naturalism of an attack. We compute either the", "type": "text" } ], "index": 33 }, { "bbox": [ 106, 558, 504, 570 ], "spans": [ { "bbox": [ 106, 558, 504, 570 ], "score": 1.0, "content": "cross correlation, the Euclidian distance, or the cosine similarity between the attacked signal and the", "type": "text" } ], "index": 34 }, { "bbox": [ 106, 569, 491, 581 ], "spans": [ { "bbox": [ 106, 569, 291, 581 ], "score": 1.0, "content": "original EEG, and average the values over the", "type": "text" }, { "bbox": [ 291, 569, 309, 580 ], "score": 0.91, "content": "N _ { c h }", "type": "inline_equation" }, { "bbox": [ 309, 569, 491, 581 ], "score": 1.0, "content": "channels and over the samples in the dataset.", "type": "text" } ], "index": 35 } ], "index": 33, "bbox_fs": [ 105, 524, 505, 581 ] }, { "type": "title", "bbox": [ 108, 593, 262, 604 ], "lines": [ { "bbox": [ 106, 593, 264, 606 ], "spans": [ { "bbox": [ 106, 593, 264, 606 ], "score": 1.0, "content": "3.2 SPATIAL PROPAGATION MODEL", "type": "text" } ], "index": 36 } ], "index": 36 }, { "type": "text", "bbox": [ 106, 613, 505, 713 ], "lines": [ { "bbox": [ 106, 613, 506, 627 ], "spans": [ { "bbox": [ 106, 613, 506, 627 ], "score": 1.0, "content": "So far, a perturbation signal was designed for every individual channel. It is unrealistic for an attacker", "type": "text" } ], "index": 37 }, { "bbox": [ 105, 624, 505, 637 ], "spans": [ { "bbox": [ 105, 624, 505, 637 ], "score": 1.0, "content": "to perturb the signal for all individual channels simultaneously; hence, we consider a more practical", "type": "text" } ], "index": 38 }, { "bbox": [ 104, 633, 506, 649 ], "spans": [ { "bbox": [ 104, 633, 256, 649 ], "score": 1.0, "content": "use case where the perturbation signal", "type": "text" }, { "bbox": [ 257, 635, 293, 646 ], "score": 0.91, "content": "\\mathbf { v } \\in \\mathbb { R } ^ { N _ { s } }", "type": "inline_equation" }, { "bbox": [ 293, 633, 506, 649 ], "score": 1.0, "content": "is emitted from one location, e.g., from an adversarial", "type": "text" } ], "index": 39 }, { "bbox": [ 105, 645, 505, 659 ], "spans": [ { "bbox": [ 105, 645, 505, 659 ], "score": 1.0, "content": "device placed on the left side of the subject or close to the left ear. More specifically, in this study, we", "type": "text" } ], "index": 40 }, { "bbox": [ 105, 658, 506, 671 ], "spans": [ { "bbox": [ 105, 658, 506, 671 ], "score": 1.0, "content": "assume that the EEG electrode at the position T9 according to the international 10-10 system (Sch),", "type": "text" } ], "index": 41 }, { "bbox": [ 105, 667, 505, 683 ], "spans": [ { "bbox": [ 105, 667, 505, 683 ], "score": 1.0, "content": "which is the closest to the left ear, senses the largest perturbation. The signal subsequently propagates", "type": "text" } ], "index": 42 }, { "bbox": [ 105, 678, 505, 693 ], "spans": [ { "bbox": [ 105, 678, 505, 693 ], "score": 1.0, "content": "over the skin to each electrode, which results in an individual magnitude and delay depending on", "type": "text" } ], "index": 43 }, { "bbox": [ 106, 690, 506, 703 ], "spans": [ { "bbox": [ 106, 690, 506, 703 ], "score": 1.0, "content": "the distance between the adversarial device and the electrode. More formally, we model the sensed", "type": "text" } ], "index": 44 }, { "bbox": [ 105, 702, 290, 713 ], "spans": [ { "bbox": [ 105, 702, 201, 713 ], "score": 1.0, "content": "perturbation at channel", "type": "text" }, { "bbox": [ 201, 702, 205, 711 ], "score": 0.75, "content": "i", "type": "inline_equation" }, { "bbox": [ 206, 702, 273, 713 ], "score": 1.0, "content": "and time instant", "type": "text" }, { "bbox": [ 273, 703, 278, 711 ], "score": 0.8, "content": "t", "type": "inline_equation" }, { "bbox": [ 278, 702, 290, 713 ], "score": 1.0, "content": "as", "type": "text" } ], "index": 45 } ], "index": 41, "bbox_fs": [ 104, 613, 506, 713 ] }, { "type": "interline_equation", "bbox": [ 204, 716, 406, 730 ], "lines": [ { "bbox": [ 204, 716, 406, 730 ], "spans": [ { "bbox": [ 204, 716, 406, 730 ], "score": 0.88, "content": "h _ { i } ( \\mathbf { v } , \\lambda _ { m } , \\lambda _ { d } ) ( t ) : = m ( l _ { i } , \\lambda _ { m } ) \\cdot \\mathbf { v } \\left( t - d ( l _ { i } , \\lambda _ { d } ) \\right) ,", "type": "interline_equation", "image_path": "376c0312852c9f41d7a3778ae583d8e40b0cf5932a3541026cd26602e02373b7.jpg" } ] } ], "index": 46, "virtual_lines": [ { "bbox": [ 204, 716, 406, 730 ], "spans": [], "index": 46 } ] } ] }, { "preproc_blocks": [ { "type": "text", "bbox": [ 107, 82, 505, 116 ], "lines": [ { "bbox": [ 105, 82, 505, 95 ], "spans": [ { "bbox": [ 105, 82, 133, 95 ], "score": 1.0, "content": "where", "type": "text" }, { "bbox": [ 134, 82, 175, 95 ], "score": 0.92, "content": "m ( l _ { i } , \\lambda _ { m } )", "type": "inline_equation" }, { "bbox": [ 175, 82, 194, 95 ], "score": 1.0, "content": "and", "type": "text" }, { "bbox": [ 194, 82, 228, 95 ], "score": 0.94, "content": "d ( l _ { i } , \\lambda _ { d } )", "type": "inline_equation" }, { "bbox": [ 229, 82, 505, 95 ], "score": 1.0, "content": "are the magnitude and the delay respectively, both of which depend", "type": "text" } ], "index": 0 }, { "bbox": [ 106, 94, 504, 105 ], "spans": [ { "bbox": [ 106, 94, 168, 105 ], "score": 1.0, "content": "on the distance", "type": "text" }, { "bbox": [ 168, 95, 175, 104 ], "score": 0.85, "content": "l _ { i }", "type": "inline_equation" }, { "bbox": [ 176, 94, 306, 105 ], "score": 1.0, "content": "and on characteristic parameters", "type": "text" }, { "bbox": [ 306, 94, 321, 105 ], "score": 0.9, "content": "\\lambda _ { m }", "type": "inline_equation" }, { "bbox": [ 321, 94, 338, 105 ], "score": 1.0, "content": "and", "type": "text" }, { "bbox": [ 339, 94, 350, 105 ], "score": 0.88, "content": "\\lambda _ { d }", "type": "inline_equation" }, { "bbox": [ 351, 94, 504, 105 ], "score": 1.0, "content": ". We define the resulting multi-channel", "type": "text" } ], "index": 1 }, { "bbox": [ 104, 101, 430, 118 ], "spans": [ { "bbox": [ 104, 101, 158, 118 ], "score": 1.0, "content": "perturbation", "type": "text" }, { "bbox": [ 158, 104, 217, 115 ], "score": 0.91, "content": "\\mathbf { V } \\in \\mathbb { R } ^ { N _ { s } \\times N _ { c h } }", "type": "inline_equation" }, { "bbox": [ 218, 101, 430, 118 ], "score": 1.0, "content": ", which is added to the multi-channel EEG signal, as", "type": "text" } ], "index": 2 } ], "index": 1 }, { "type": "interline_equation", "bbox": [ 118, 121, 476, 135 ], "lines": [ { "bbox": [ 118, 121, 476, 135 ], "spans": [ { "bbox": [ 118, 121, 476, 135 ], "score": 0.9, "content": "\\begin{array} { r } { { \\bf V } ( \\lambda _ { m } , \\lambda _ { d } ) = H ( { \\bf v } , \\lambda _ { m } , \\lambda _ { d } ) : = \\left( h _ { 0 } ( { \\bf v } , \\lambda _ { m } , \\lambda _ { d } ) , h _ { 1 } ( { \\bf v } , \\lambda _ { m } , \\lambda _ { d } ) , . . . , h _ { N _ { c h } - 1 } ( { \\bf v } , \\lambda _ { m } , \\lambda _ { d } ) \\right) . } \\end{array}", "type": "interline_equation", "image_path": "de96e5a139b1fc23afd99dccaff6c949ce6e68096635c4b34caf36537f06be33.jpg" } ] } ], "index": 3, "virtual_lines": [ { "bbox": [ 118, 121, 476, 135 ], "spans": [], "index": 3 } ] }, { "type": "text", "bbox": [ 106, 146, 505, 180 ], "lines": [ { "bbox": [ 106, 146, 505, 159 ], "spans": [ { "bbox": [ 106, 146, 204, 159 ], "score": 1.0, "content": "We estimate the distance", "type": "text" }, { "bbox": [ 205, 147, 212, 158 ], "score": 0.86, "content": "l _ { i }", "type": "inline_equation" }, { "bbox": [ 212, 146, 505, 159 ], "score": 1.0, "content": "between the electrode at position T9 and the remaining, attacked positions", "type": "text" } ], "index": 4 }, { "bbox": [ 106, 158, 505, 170 ], "spans": [ { "bbox": [ 106, 158, 337, 170 ], "score": 1.0, "content": "using the 10-10 system and a head model with a radius of", "type": "text" }, { "bbox": [ 338, 158, 365, 168 ], "score": 0.61, "content": "8 . 7 \\mathrm { c m }", "type": "inline_equation" }, { "bbox": [ 366, 158, 505, 170 ], "score": 1.0, "content": "(Algazi et al., 2001). We decouple", "type": "text" } ], "index": 5 }, { "bbox": [ 105, 168, 506, 182 ], "spans": [ { "bbox": [ 105, 168, 506, 182 ], "score": 1.0, "content": "the distance-dependent modeling of the magnitude and delay, explained in the following paragraphs.", "type": "text" } ], "index": 6 } ], "index": 5 }, { "type": "text", "bbox": [ 106, 192, 505, 236 ], "lines": [ { "bbox": [ 106, 192, 505, 204 ], "spans": [ { "bbox": [ 106, 192, 505, 204 ], "score": 1.0, "content": "Magnitude. For modeling the magnitude, we assume that the adversarial device injects or induces", "type": "text" } ], "index": 7 }, { "bbox": [ 105, 203, 505, 215 ], "spans": [ { "bbox": [ 105, 203, 144, 215 ], "score": 1.0, "content": "a current", "type": "text" }, { "bbox": [ 144, 204, 151, 213 ], "score": 0.73, "content": "I", "type": "inline_equation" }, { "bbox": [ 151, 203, 234, 215 ], "score": 1.0, "content": ", yielding a potential", "type": "text" }, { "bbox": [ 235, 204, 244, 213 ], "score": 0.79, "content": "V", "type": "inline_equation" }, { "bbox": [ 244, 203, 505, 215 ], "score": 1.0, "content": "measured near T9. The current propagates over the head surface", "type": "text" } ], "index": 8 }, { "bbox": [ 106, 214, 505, 227 ], "spans": [ { "bbox": [ 106, 214, 505, 227 ], "score": 1.0, "content": "through the skin to each of the remaining attacked electrodes, which can be modeled as a cylindrical", "type": "text" } ], "index": 9 }, { "bbox": [ 105, 224, 201, 237 ], "spans": [ { "bbox": [ 105, 224, 201, 237 ], "score": 1.0, "content": "resistor with resistance", "type": "text" } ], "index": 10 } ], "index": 8.5 }, { "type": "interline_equation", "bbox": [ 282, 239, 327, 264 ], "lines": [ { "bbox": [ 282, 239, 327, 264 ], "spans": [ { "bbox": [ 282, 239, 327, 264 ], "score": 0.93, "content": "R _ { i } = \\frac { l _ { i } } { \\sigma A } ,", "type": "interline_equation", "image_path": "df7117bdb4d5c93cccf2e90b25cc3c804499313daf11d78a2ed1cb0219bdb070.jpg" } ] } ], "index": 11, "virtual_lines": [ { "bbox": [ 282, 239, 327, 264 ], "spans": [], "index": 11 } ] }, { "type": "text", "bbox": [ 107, 268, 505, 302 ], "lines": [ { "bbox": [ 106, 268, 506, 280 ], "spans": [ { "bbox": [ 106, 268, 132, 280 ], "score": 1.0, "content": "where", "type": "text" }, { "bbox": [ 132, 271, 140, 279 ], "score": 0.78, "content": "\\sigma", "type": "inline_equation" }, { "bbox": [ 140, 268, 506, 280 ], "score": 1.0, "content": "is the conductivity of the skin which can be in the range of [0.28, 0.87] Siemens/m (Vorwerk", "type": "text" } ], "index": 12 }, { "bbox": [ 105, 279, 507, 293 ], "spans": [ { "bbox": [ 105, 279, 176, 293 ], "score": 1.0, "content": "et al., 2019), and", "type": "text" }, { "bbox": [ 176, 280, 184, 290 ], "score": 0.81, "content": "A", "type": "inline_equation" }, { "bbox": [ 185, 279, 420, 293 ], "score": 1.0, "content": "is the area of the skin conductor. The potential at electrode", "type": "text" }, { "bbox": [ 420, 281, 425, 290 ], "score": 0.77, "content": "i", "type": "inline_equation" }, { "bbox": [ 425, 279, 435, 293 ], "score": 1.0, "content": "is", "type": "text" }, { "bbox": [ 436, 280, 502, 291 ], "score": 0.92, "content": "V _ { i } = V - I \\cdot R _ { i }", "type": "inline_equation" }, { "bbox": [ 502, 279, 507, 293 ], "score": 1.0, "content": ",", "type": "text" } ], "index": 13 }, { "bbox": [ 106, 291, 288, 303 ], "spans": [ { "bbox": [ 106, 291, 288, 303 ], "score": 1.0, "content": "and hence the magnitude can be described as", "type": "text" } ], "index": 14 } ], "index": 13 }, { "type": "interline_equation", "bbox": [ 196, 307, 414, 332 ], "lines": [ { "bbox": [ 196, 307, 414, 332 ], "spans": [ { "bbox": [ 196, 307, 414, 332 ], "score": 0.92, "content": "m ( l _ { i } , \\lambda _ { m } ) = 1 - \\frac { V - V _ { i } } { V } = 1 - \\frac { I } { V \\sigma A } l _ { i } = 1 - \\lambda _ { m } l _ { i } ,", "type": "interline_equation", "image_path": "be06049f08f97d940ab4e03b5c05093432b85058b4a81d8046d097f375f7225f.jpg" } ] } ], "index": 15, "virtual_lines": [ { "bbox": [ 196, 307, 414, 332 ], "spans": [], "index": 15 } ] }, { "type": "text", "bbox": [ 106, 335, 506, 426 ], "lines": [ { "bbox": [ 105, 335, 504, 350 ], "spans": [ { "bbox": [ 105, 335, 223, 350 ], "score": 1.0, "content": "where we further constrain", "type": "text" }, { "bbox": [ 223, 336, 312, 349 ], "score": 0.92, "content": "0 \\leq m ( l _ { i } , \\lambda _ { m } ) \\leq 1", "type": "inline_equation" }, { "bbox": [ 312, 335, 489, 350 ], "score": 1.0, "content": ". The characteristic magnitude parameter", "type": "text" }, { "bbox": [ 489, 337, 504, 348 ], "score": 0.89, "content": "\\lambda _ { m }", "type": "inline_equation" } ], "index": 16 }, { "bbox": [ 104, 347, 506, 362 ], "spans": [ { "bbox": [ 104, 347, 506, 362 ], "score": 1.0, "content": "represents the complex interplay between input current, voltage, conductivity, and area, covering", "type": "text" } ], "index": 17 }, { "bbox": [ 104, 358, 506, 371 ], "spans": [ { "bbox": [ 104, 358, 450, 371 ], "score": 1.0, "content": "various attack scenarios. We consider different characteristic magnitude parameters", "type": "text" }, { "bbox": [ 450, 358, 502, 370 ], "score": 0.79, "content": "\\lambda _ { m } \\in [ 1 , 1 5 ]", "type": "inline_equation" }, { "bbox": [ 503, 358, 506, 371 ], "score": 1.0, "content": ".", "type": "text" } ], "index": 18 }, { "bbox": [ 105, 369, 506, 383 ], "spans": [ { "bbox": [ 105, 369, 140, 383 ], "score": 1.0, "content": "A large", "type": "text" }, { "bbox": [ 140, 370, 154, 381 ], "score": 0.89, "content": "\\lambda _ { m }", "type": "inline_equation" }, { "bbox": [ 155, 369, 506, 383 ], "score": 1.0, "content": "represents cases with large attenuation and limited propagation, i.e., a limited set of", "type": "text" } ], "index": 19 }, { "bbox": [ 106, 381, 506, 393 ], "spans": [ { "bbox": [ 106, 381, 387, 393 ], "score": 1.0, "content": "neighboring electrodes sense the perturbation. Conversely, a small", "type": "text" }, { "bbox": [ 387, 381, 402, 392 ], "score": 0.9, "content": "\\lambda _ { m }", "type": "inline_equation" }, { "bbox": [ 402, 381, 506, 393 ], "score": 1.0, "content": "covers cases with lower", "type": "text" } ], "index": 20 }, { "bbox": [ 105, 392, 506, 404 ], "spans": [ { "bbox": [ 105, 392, 506, 404 ], "score": 1.0, "content": "attenuation where the perturbation can propagate further and infects all electrodes. We consider also", "type": "text" } ], "index": 21 }, { "bbox": [ 105, 402, 507, 415 ], "spans": [ { "bbox": [ 105, 402, 468, 415 ], "score": 1.0, "content": "an intermediate case where around half of the electrodes are affected by the attack with", "type": "text" }, { "bbox": [ 469, 403, 503, 414 ], "score": 0.91, "content": "\\lambda _ { m } = 5", "type": "inline_equation" }, { "bbox": [ 503, 402, 507, 415 ], "score": 1.0, "content": ".", "type": "text" } ], "index": 22 }, { "bbox": [ 105, 414, 486, 426 ], "spans": [ { "bbox": [ 105, 414, 486, 426 ], "score": 1.0, "content": "Appendix B provides examples of the magnitude of the spatial propagation on the head model.", "type": "text" } ], "index": 23 } ], "index": 19.5 }, { "type": "text", "bbox": [ 106, 437, 505, 537 ], "lines": [ { "bbox": [ 105, 437, 506, 450 ], "spans": [ { "bbox": [ 105, 437, 506, 450 ], "score": 1.0, "content": "Delay. The propagation of a signal on the head surface yields a position-dependent phase angle or", "type": "text" } ], "index": 24 }, { "bbox": [ 106, 448, 505, 461 ], "spans": [ { "bbox": [ 106, 448, 505, 461 ], "score": 1.0, "content": "delay, as shown by experimental measurements of related studies (Plutchik & Hirsch, 1963; Qiao", "type": "text" } ], "index": 25 }, { "bbox": [ 106, 460, 506, 471 ], "spans": [ { "bbox": [ 106, 460, 506, 471 ], "score": 1.0, "content": "et al., 1994). The delay stems from a combination of resistive and capacitive components that are", "type": "text" } ], "index": 26 }, { "bbox": [ 105, 470, 506, 482 ], "spans": [ { "bbox": [ 105, 470, 506, 482 ], "score": 1.0, "content": "encountered during the propagation of the signal, which can be modeled as an RC-circuit with", "type": "text" } ], "index": 27 }, { "bbox": [ 105, 481, 506, 493 ], "spans": [ { "bbox": [ 105, 482, 149, 493 ], "score": 1.0, "content": "resistance", "type": "text" }, { "bbox": [ 149, 482, 158, 491 ], "score": 0.77, "content": "R", "type": "inline_equation" }, { "bbox": [ 158, 482, 198, 493 ], "score": 1.0, "content": ", capacity", "type": "text" }, { "bbox": [ 198, 482, 207, 491 ], "score": 0.79, "content": "C", "type": "inline_equation" }, { "bbox": [ 207, 482, 285, 493 ], "score": 1.0, "content": ", and time constant", "type": "text" }, { "bbox": [ 285, 481, 327, 491 ], "score": 0.91, "content": "\\tau = R \\cdot C", "type": "inline_equation" }, { "bbox": [ 328, 482, 506, 493 ], "score": 1.0, "content": "that relates to the group delay. Specifically,", "type": "text" } ], "index": 28 }, { "bbox": [ 106, 492, 506, 505 ], "spans": [ { "bbox": [ 106, 492, 506, 505 ], "score": 1.0, "content": "the contacts between the electrodes and the skin are predominantly capacitive whereas the skin itself", "type": "text" } ], "index": 29 }, { "bbox": [ 104, 502, 505, 517 ], "spans": [ { "bbox": [ 104, 502, 505, 517 ], "score": 1.0, "content": "is both resistive and capacitive (Kim et al., 2010). As explained in the previous part, an increasing", "type": "text" } ], "index": 30 }, { "bbox": [ 105, 513, 505, 527 ], "spans": [ { "bbox": [ 105, 513, 428, 527 ], "score": 1.0, "content": "distance between the attacker and the target electrode yields a larger resistance", "type": "text" }, { "bbox": [ 428, 515, 437, 524 ], "score": 0.57, "content": "R", "type": "inline_equation" }, { "bbox": [ 437, 513, 505, 527 ], "score": 1.0, "content": ". As a result, the", "type": "text" } ], "index": 31 }, { "bbox": [ 106, 525, 279, 537 ], "spans": [ { "bbox": [ 106, 525, 162, 537 ], "score": 1.0, "content": "time constant", "type": "text" }, { "bbox": [ 162, 527, 169, 535 ], "score": 0.76, "content": "\\tau", "type": "inline_equation" }, { "bbox": [ 170, 525, 279, 537 ], "score": 1.0, "content": "and the delay increase too.", "type": "text" } ], "index": 32 } ], "index": 28 }, { "type": "text", "bbox": [ 106, 541, 506, 619 ], "lines": [ { "bbox": [ 106, 542, 506, 554 ], "spans": [ { "bbox": [ 106, 542, 506, 554 ], "score": 1.0, "content": "Here, we model a linear distance-delay relation. We rely on a study by Plutchik & Hirsch (1963),", "type": "text" } ], "index": 33 }, { "bbox": [ 105, 552, 506, 566 ], "spans": [ { "bbox": [ 105, 552, 506, 566 ], "score": 1.0, "content": "which conducted human skin impedance and phase angle measurements by placing electrodes at", "type": "text" } ], "index": 34 }, { "bbox": [ 105, 564, 506, 577 ], "spans": [ { "bbox": [ 105, 564, 219, 577 ], "score": 1.0, "content": "an approximate distance of", "type": "text" }, { "bbox": [ 219, 564, 245, 574 ], "score": 0.68, "content": "1 0 \\mathrm { c m }", "type": "inline_equation" }, { "bbox": [ 245, 564, 457, 577 ], "score": 1.0, "content": "and applying voltages with frequencies in the range", "type": "text" }, { "bbox": [ 458, 564, 503, 574 ], "score": 0.78, "content": "2 { \\mathrm { - } } 1 0 0 0 \\mathrm { { H z } }", "type": "inline_equation" }, { "bbox": [ 504, 564, 506, 577 ], "score": 1.0, "content": ".", "type": "text" } ], "index": 35 }, { "bbox": [ 105, 575, 506, 588 ], "spans": [ { "bbox": [ 105, 575, 506, 588 ], "score": 1.0, "content": "When assuming a linear frequency-phase relation in low-frequency region (Qiao et al., 1994), one", "type": "text" } ], "index": 36 }, { "bbox": [ 106, 586, 505, 598 ], "spans": [ { "bbox": [ 106, 586, 234, 598 ], "score": 1.0, "content": "can derive the group delay to be", "type": "text" }, { "bbox": [ 234, 586, 261, 596 ], "score": 0.43, "content": "2 . 8 \\mathrm { m s }", "type": "inline_equation" }, { "bbox": [ 261, 586, 414, 598 ], "score": 1.0, "content": "when considering a measured angle of", "type": "text" }, { "bbox": [ 414, 586, 429, 596 ], "score": 0.88, "content": "1 0 ^ { \\circ }", "type": "inline_equation" }, { "bbox": [ 430, 586, 440, 598 ], "score": 1.0, "content": "at", "type": "text" }, { "bbox": [ 441, 586, 464, 596 ], "score": 0.72, "content": "1 0 \\mathrm { H z }", "type": "inline_equation" }, { "bbox": [ 465, 586, 505, 598 ], "score": 1.0, "content": ". As those", "type": "text" } ], "index": 37 }, { "bbox": [ 104, 596, 506, 611 ], "spans": [ { "bbox": [ 104, 596, 506, 611 ], "score": 1.0, "content": "measurements were conducted for only one distance, we extrapolate the delay for the remaining", "type": "text" } ], "index": 38 }, { "bbox": [ 106, 609, 267, 620 ], "spans": [ { "bbox": [ 106, 609, 267, 620 ], "score": 1.0, "content": "distances using a rectified linear model:", "type": "text" } ], "index": 39 } ], "index": 36 }, { "type": "interline_equation", "bbox": [ 171, 624, 438, 639 ], "lines": [ { "bbox": [ 171, 624, 438, 639 ], "spans": [ { "bbox": [ 171, 624, 438, 639 ], "score": 0.89, "content": "\\lambda _ { d } \\cdot ( l _ { i } - l _ { 0 } ) > 0 \\uparrow d ( l _ { i } , \\lambda _ { d } ) = \\lambda _ { d } \\cdot ( l _ { i } - l _ { 0 } ) + d _ { 0 } : d ( l _ { i } , \\lambda _ { d } ) = 0 ,", "type": "interline_equation", "image_path": "8c109f14baba8fdb3fa4ca00a590e185e17699666a46300fe54ae3c09558dc5d.jpg" } ] } ], "index": 40, "virtual_lines": [ { "bbox": [ 171, 624, 438, 639 ], "spans": [], "index": 40 } ] }, { "type": "text", "bbox": [ 106, 643, 506, 732 ], "lines": [ { "bbox": [ 105, 643, 507, 657 ], "spans": [ { "bbox": [ 105, 643, 133, 657 ], "score": 1.0, "content": "where", "type": "text" }, { "bbox": [ 133, 644, 183, 655 ], "score": 0.9, "content": "d _ { 0 } = 2 . 8 \\mathrm { m s }", "type": "inline_equation" }, { "bbox": [ 184, 643, 277, 657 ], "score": 1.0, "content": "is the delay at distance", "type": "text" }, { "bbox": [ 278, 644, 324, 655 ], "score": 0.93, "content": "l _ { 0 } = 1 0 \\mathrm { c m }", "type": "inline_equation" }, { "bbox": [ 324, 643, 507, 657 ], "score": 1.0, "content": ". The delay depends not only on the distance,", "type": "text" } ], "index": 41 }, { "bbox": [ 106, 655, 505, 667 ], "spans": [ { "bbox": [ 106, 655, 505, 667 ], "score": 1.0, "content": "but also on other parameters such as the electrode-to-skin contact, the humidity of the skin, etc. To", "type": "text" } ], "index": 42 }, { "bbox": [ 105, 665, 506, 678 ], "spans": [ { "bbox": [ 105, 665, 506, 678 ], "score": 1.0, "content": "this end, we evaluate the propagation of the attack with different characteristic delay parameters", "type": "text" } ], "index": 43 }, { "bbox": [ 106, 677, 506, 689 ], "spans": [ { "bbox": [ 106, 677, 192, 689 ], "score": 0.88, "content": "\\lambda _ { d } \\in \\left[ 0 . 1 , 0 . 5 6 3 \\right] \\mathrm { s / m }", "type": "inline_equation" }, { "bbox": [ 192, 677, 218, 689 ], "score": 1.0, "content": ". With", "type": "text" }, { "bbox": [ 218, 677, 256, 688 ], "score": 0.93, "content": "\\lambda _ { d } = 0 . 1", "type": "inline_equation" }, { "bbox": [ 257, 677, 506, 689 ], "score": 1.0, "content": "we cover the cases where very little delay happens, while the", "type": "text" } ], "index": 44 }, { "bbox": [ 105, 688, 506, 700 ], "spans": [ { "bbox": [ 105, 688, 183, 700 ], "score": 1.0, "content": "largest considered", "type": "text" }, { "bbox": [ 183, 688, 249, 699 ], "score": 0.9, "content": "\\lambda _ { d } = 0 . 5 6 3 \\mathrm { s / m }", "type": "inline_equation" }, { "bbox": [ 249, 688, 365, 700 ], "score": 1.0, "content": "yields a maximum delay of", "type": "text" }, { "bbox": [ 365, 688, 385, 698 ], "score": 0.34, "content": "0 . 1 \\mathrm { s }", "type": "inline_equation" }, { "bbox": [ 385, 688, 506, 700 ], "score": 1.0, "content": "at the farthest electrode T10,", "type": "text" } ], "index": 45 }, { "bbox": [ 104, 698, 507, 712 ], "spans": [ { "bbox": [ 104, 698, 507, 712 ], "score": 1.0, "content": "which is in alignment with the observed EEG measurements (Merlet et al., 2013; Sazgar & Young,", "type": "text" } ], "index": 46 }, { "bbox": [ 106, 710, 505, 722 ], "spans": [ { "bbox": [ 106, 710, 185, 722 ], "score": 1.0, "content": "2019). Similarly to", "type": "text" }, { "bbox": [ 185, 710, 199, 721 ], "score": 0.88, "content": "\\lambda _ { m }", "type": "inline_equation" }, { "bbox": [ 199, 710, 389, 722 ], "score": 1.0, "content": ", we showcase also for an intermediate value of", "type": "text" }, { "bbox": [ 389, 710, 427, 721 ], "score": 0.91, "content": "\\lambda _ { d } = 0 . 3", "type": "inline_equation" }, { "bbox": [ 427, 710, 505, 722 ], "score": 1.0, "content": "which corresponds", "type": "text" } ], "index": 47 }, { "bbox": [ 106, 721, 228, 733 ], "spans": [ { "bbox": [ 106, 721, 158, 733 ], "score": 1.0, "content": "to a delay of", "type": "text" }, { "bbox": [ 159, 721, 195, 731 ], "score": 0.27, "content": "0 . 0 5 3 \\mathrm { m s }", "type": "inline_equation" }, { "bbox": [ 196, 721, 228, 733 ], "score": 1.0, "content": "at T10.", "type": "text" } ], "index": 48 } ], "index": 44.5 } ], "page_idx": 4, "page_size": [ 612, 792 ], "discarded_blocks": [ { "type": "discarded", "bbox": [ 106, 26, 308, 37 ], "lines": [ { "bbox": [ 106, 25, 309, 38 ], "spans": [ { "bbox": [ 106, 25, 309, 38 ], "score": 1.0, "content": "Under review as a conference paper at ICLR 2022", "type": "text" } ] } ] }, { "type": "discarded", "bbox": [ 302, 751, 308, 760 ], "lines": [ { "bbox": [ 302, 750, 309, 763 ], "spans": [ { "bbox": [ 302, 750, 309, 763 ], "score": 1.0, "content": "5", "type": "text" } ] } ] } ], "para_blocks": [ { "type": "text", "bbox": [ 107, 82, 505, 116 ], "lines": [ { "bbox": [ 105, 82, 505, 95 ], "spans": [ { "bbox": [ 105, 82, 133, 95 ], "score": 1.0, "content": "where", "type": "text" }, { "bbox": [ 134, 82, 175, 95 ], "score": 0.92, "content": "m ( l _ { i } , \\lambda _ { m } )", "type": "inline_equation" }, { "bbox": [ 175, 82, 194, 95 ], "score": 1.0, "content": "and", "type": "text" }, { "bbox": [ 194, 82, 228, 95 ], "score": 0.94, "content": "d ( l _ { i } , \\lambda _ { d } )", "type": "inline_equation" }, { "bbox": [ 229, 82, 505, 95 ], "score": 1.0, "content": "are the magnitude and the delay respectively, both of which depend", "type": "text" } ], "index": 0 }, { "bbox": [ 106, 94, 504, 105 ], "spans": [ { "bbox": [ 106, 94, 168, 105 ], "score": 1.0, "content": "on the distance", "type": "text" }, { "bbox": [ 168, 95, 175, 104 ], "score": 0.85, "content": "l _ { i }", "type": "inline_equation" }, { "bbox": [ 176, 94, 306, 105 ], "score": 1.0, "content": "and on characteristic parameters", "type": "text" }, { "bbox": [ 306, 94, 321, 105 ], "score": 0.9, "content": "\\lambda _ { m }", "type": "inline_equation" }, { "bbox": [ 321, 94, 338, 105 ], "score": 1.0, "content": "and", "type": "text" }, { "bbox": [ 339, 94, 350, 105 ], "score": 0.88, "content": "\\lambda _ { d }", "type": "inline_equation" }, { "bbox": [ 351, 94, 504, 105 ], "score": 1.0, "content": ". We define the resulting multi-channel", "type": "text" } ], "index": 1 }, { "bbox": [ 104, 101, 430, 118 ], "spans": [ { "bbox": [ 104, 101, 158, 118 ], "score": 1.0, "content": "perturbation", "type": "text" }, { "bbox": [ 158, 104, 217, 115 ], "score": 0.91, "content": "\\mathbf { V } \\in \\mathbb { R } ^ { N _ { s } \\times N _ { c h } }", "type": "inline_equation" }, { "bbox": [ 218, 101, 430, 118 ], "score": 1.0, "content": ", which is added to the multi-channel EEG signal, as", "type": "text" } ], "index": 2 } ], "index": 1, "bbox_fs": [ 104, 82, 505, 118 ] }, { "type": "interline_equation", "bbox": [ 118, 121, 476, 135 ], "lines": [ { "bbox": [ 118, 121, 476, 135 ], "spans": [ { "bbox": [ 118, 121, 476, 135 ], "score": 0.9, "content": "\\begin{array} { r } { { \\bf V } ( \\lambda _ { m } , \\lambda _ { d } ) = H ( { \\bf v } , \\lambda _ { m } , \\lambda _ { d } ) : = \\left( h _ { 0 } ( { \\bf v } , \\lambda _ { m } , \\lambda _ { d } ) , h _ { 1 } ( { \\bf v } , \\lambda _ { m } , \\lambda _ { d } ) , . . . , h _ { N _ { c h } - 1 } ( { \\bf v } , \\lambda _ { m } , \\lambda _ { d } ) \\right) . } \\end{array}", "type": "interline_equation", "image_path": "de96e5a139b1fc23afd99dccaff6c949ce6e68096635c4b34caf36537f06be33.jpg" } ] } ], "index": 3, "virtual_lines": [ { "bbox": [ 118, 121, 476, 135 ], "spans": [], "index": 3 } ] }, { "type": "text", "bbox": [ 106, 146, 505, 180 ], "lines": [ { "bbox": [ 106, 146, 505, 159 ], "spans": [ { "bbox": [ 106, 146, 204, 159 ], "score": 1.0, "content": "We estimate the distance", "type": "text" }, { "bbox": [ 205, 147, 212, 158 ], "score": 0.86, "content": "l _ { i }", "type": "inline_equation" }, { "bbox": [ 212, 146, 505, 159 ], "score": 1.0, "content": "between the electrode at position T9 and the remaining, attacked positions", "type": "text" } ], "index": 4 }, { "bbox": [ 106, 158, 505, 170 ], "spans": [ { "bbox": [ 106, 158, 337, 170 ], "score": 1.0, "content": "using the 10-10 system and a head model with a radius of", "type": "text" }, { "bbox": [ 338, 158, 365, 168 ], "score": 0.61, "content": "8 . 7 \\mathrm { c m }", "type": "inline_equation" }, { "bbox": [ 366, 158, 505, 170 ], "score": 1.0, "content": "(Algazi et al., 2001). We decouple", "type": "text" } ], "index": 5 }, { "bbox": [ 105, 168, 506, 182 ], "spans": [ { "bbox": [ 105, 168, 506, 182 ], "score": 1.0, "content": "the distance-dependent modeling of the magnitude and delay, explained in the following paragraphs.", "type": "text" } ], "index": 6 } ], "index": 5, "bbox_fs": [ 105, 146, 506, 182 ] }, { "type": "text", "bbox": [ 106, 192, 505, 236 ], "lines": [ { "bbox": [ 106, 192, 505, 204 ], "spans": [ { "bbox": [ 106, 192, 505, 204 ], "score": 1.0, "content": "Magnitude. For modeling the magnitude, we assume that the adversarial device injects or induces", "type": "text" } ], "index": 7 }, { "bbox": [ 105, 203, 505, 215 ], "spans": [ { "bbox": [ 105, 203, 144, 215 ], "score": 1.0, "content": "a current", "type": "text" }, { "bbox": [ 144, 204, 151, 213 ], "score": 0.73, "content": "I", "type": "inline_equation" }, { "bbox": [ 151, 203, 234, 215 ], "score": 1.0, "content": ", yielding a potential", "type": "text" }, { "bbox": [ 235, 204, 244, 213 ], "score": 0.79, "content": "V", "type": "inline_equation" }, { "bbox": [ 244, 203, 505, 215 ], "score": 1.0, "content": "measured near T9. The current propagates over the head surface", "type": "text" } ], "index": 8 }, { "bbox": [ 106, 214, 505, 227 ], "spans": [ { "bbox": [ 106, 214, 505, 227 ], "score": 1.0, "content": "through the skin to each of the remaining attacked electrodes, which can be modeled as a cylindrical", "type": "text" } ], "index": 9 }, { "bbox": [ 105, 224, 201, 237 ], "spans": [ { "bbox": [ 105, 224, 201, 237 ], "score": 1.0, "content": "resistor with resistance", "type": "text" } ], "index": 10 } ], "index": 8.5, "bbox_fs": [ 105, 192, 505, 237 ] }, { "type": "interline_equation", "bbox": [ 282, 239, 327, 264 ], "lines": [ { "bbox": [ 282, 239, 327, 264 ], "spans": [ { "bbox": [ 282, 239, 327, 264 ], "score": 0.93, "content": "R _ { i } = \\frac { l _ { i } } { \\sigma A } ,", "type": "interline_equation", "image_path": "df7117bdb4d5c93cccf2e90b25cc3c804499313daf11d78a2ed1cb0219bdb070.jpg" } ] } ], "index": 11, "virtual_lines": [ { "bbox": [ 282, 239, 327, 264 ], "spans": [], "index": 11 } ] }, { "type": "text", "bbox": [ 107, 268, 505, 302 ], "lines": [ { "bbox": [ 106, 268, 506, 280 ], "spans": [ { "bbox": [ 106, 268, 132, 280 ], "score": 1.0, "content": "where", "type": "text" }, { "bbox": [ 132, 271, 140, 279 ], "score": 0.78, "content": "\\sigma", "type": "inline_equation" }, { "bbox": [ 140, 268, 506, 280 ], "score": 1.0, "content": "is the conductivity of the skin which can be in the range of [0.28, 0.87] Siemens/m (Vorwerk", "type": "text" } ], "index": 12 }, { "bbox": [ 105, 279, 507, 293 ], "spans": [ { "bbox": [ 105, 279, 176, 293 ], "score": 1.0, "content": "et al., 2019), and", "type": "text" }, { "bbox": [ 176, 280, 184, 290 ], "score": 0.81, "content": "A", "type": "inline_equation" }, { "bbox": [ 185, 279, 420, 293 ], "score": 1.0, "content": "is the area of the skin conductor. The potential at electrode", "type": "text" }, { "bbox": [ 420, 281, 425, 290 ], "score": 0.77, "content": "i", "type": "inline_equation" }, { "bbox": [ 425, 279, 435, 293 ], "score": 1.0, "content": "is", "type": "text" }, { "bbox": [ 436, 280, 502, 291 ], "score": 0.92, "content": "V _ { i } = V - I \\cdot R _ { i }", "type": "inline_equation" }, { "bbox": [ 502, 279, 507, 293 ], "score": 1.0, "content": ",", "type": "text" } ], "index": 13 }, { "bbox": [ 106, 291, 288, 303 ], "spans": [ { "bbox": [ 106, 291, 288, 303 ], "score": 1.0, "content": "and hence the magnitude can be described as", "type": "text" } ], "index": 14 } ], "index": 13, "bbox_fs": [ 105, 268, 507, 303 ] }, { "type": "interline_equation", "bbox": [ 196, 307, 414, 332 ], "lines": [ { "bbox": [ 196, 307, 414, 332 ], "spans": [ { "bbox": [ 196, 307, 414, 332 ], "score": 0.92, "content": "m ( l _ { i } , \\lambda _ { m } ) = 1 - \\frac { V - V _ { i } } { V } = 1 - \\frac { I } { V \\sigma A } l _ { i } = 1 - \\lambda _ { m } l _ { i } ,", "type": "interline_equation", "image_path": "be06049f08f97d940ab4e03b5c05093432b85058b4a81d8046d097f375f7225f.jpg" } ] } ], "index": 15, "virtual_lines": [ { "bbox": [ 196, 307, 414, 332 ], "spans": [], "index": 15 } ] }, { "type": "text", "bbox": [ 106, 335, 506, 426 ], "lines": [ { "bbox": [ 105, 335, 504, 350 ], "spans": [ { "bbox": [ 105, 335, 223, 350 ], "score": 1.0, "content": "where we further constrain", "type": "text" }, { "bbox": [ 223, 336, 312, 349 ], "score": 0.92, "content": "0 \\leq m ( l _ { i } , \\lambda _ { m } ) \\leq 1", "type": "inline_equation" }, { "bbox": [ 312, 335, 489, 350 ], "score": 1.0, "content": ". The characteristic magnitude parameter", "type": "text" }, { "bbox": [ 489, 337, 504, 348 ], "score": 0.89, "content": "\\lambda _ { m }", "type": "inline_equation" } ], "index": 16 }, { "bbox": [ 104, 347, 506, 362 ], "spans": [ { "bbox": [ 104, 347, 506, 362 ], "score": 1.0, "content": "represents the complex interplay between input current, voltage, conductivity, and area, covering", "type": "text" } ], "index": 17 }, { "bbox": [ 104, 358, 506, 371 ], "spans": [ { "bbox": [ 104, 358, 450, 371 ], "score": 1.0, "content": "various attack scenarios. We consider different characteristic magnitude parameters", "type": "text" }, { "bbox": [ 450, 358, 502, 370 ], "score": 0.79, "content": "\\lambda _ { m } \\in [ 1 , 1 5 ]", "type": "inline_equation" }, { "bbox": [ 503, 358, 506, 371 ], "score": 1.0, "content": ".", "type": "text" } ], "index": 18 }, { "bbox": [ 105, 369, 506, 383 ], "spans": [ { "bbox": [ 105, 369, 140, 383 ], "score": 1.0, "content": "A large", "type": "text" }, { "bbox": [ 140, 370, 154, 381 ], "score": 0.89, "content": "\\lambda _ { m }", "type": "inline_equation" }, { "bbox": [ 155, 369, 506, 383 ], "score": 1.0, "content": "represents cases with large attenuation and limited propagation, i.e., a limited set of", "type": "text" } ], "index": 19 }, { "bbox": [ 106, 381, 506, 393 ], "spans": [ { "bbox": [ 106, 381, 387, 393 ], "score": 1.0, "content": "neighboring electrodes sense the perturbation. Conversely, a small", "type": "text" }, { "bbox": [ 387, 381, 402, 392 ], "score": 0.9, "content": "\\lambda _ { m }", "type": "inline_equation" }, { "bbox": [ 402, 381, 506, 393 ], "score": 1.0, "content": "covers cases with lower", "type": "text" } ], "index": 20 }, { "bbox": [ 105, 392, 506, 404 ], "spans": [ { "bbox": [ 105, 392, 506, 404 ], "score": 1.0, "content": "attenuation where the perturbation can propagate further and infects all electrodes. We consider also", "type": "text" } ], "index": 21 }, { "bbox": [ 105, 402, 507, 415 ], "spans": [ { "bbox": [ 105, 402, 468, 415 ], "score": 1.0, "content": "an intermediate case where around half of the electrodes are affected by the attack with", "type": "text" }, { "bbox": [ 469, 403, 503, 414 ], "score": 0.91, "content": "\\lambda _ { m } = 5", "type": "inline_equation" }, { "bbox": [ 503, 402, 507, 415 ], "score": 1.0, "content": ".", "type": "text" } ], "index": 22 }, { "bbox": [ 105, 414, 486, 426 ], "spans": [ { "bbox": [ 105, 414, 486, 426 ], "score": 1.0, "content": "Appendix B provides examples of the magnitude of the spatial propagation on the head model.", "type": "text" } ], "index": 23 } ], "index": 19.5, "bbox_fs": [ 104, 335, 507, 426 ] }, { "type": "text", "bbox": [ 106, 437, 505, 537 ], "lines": [ { "bbox": [ 105, 437, 506, 450 ], "spans": [ { "bbox": [ 105, 437, 506, 450 ], "score": 1.0, "content": "Delay. The propagation of a signal on the head surface yields a position-dependent phase angle or", "type": "text" } ], "index": 24 }, { "bbox": [ 106, 448, 505, 461 ], "spans": [ { "bbox": [ 106, 448, 505, 461 ], "score": 1.0, "content": "delay, as shown by experimental measurements of related studies (Plutchik & Hirsch, 1963; Qiao", "type": "text" } ], "index": 25 }, { "bbox": [ 106, 460, 506, 471 ], "spans": [ { "bbox": [ 106, 460, 506, 471 ], "score": 1.0, "content": "et al., 1994). The delay stems from a combination of resistive and capacitive components that are", "type": "text" } ], "index": 26 }, { "bbox": [ 105, 470, 506, 482 ], "spans": [ { "bbox": [ 105, 470, 506, 482 ], "score": 1.0, "content": "encountered during the propagation of the signal, which can be modeled as an RC-circuit with", "type": "text" } ], "index": 27 }, { "bbox": [ 105, 481, 506, 493 ], "spans": [ { "bbox": [ 105, 482, 149, 493 ], "score": 1.0, "content": "resistance", "type": "text" }, { "bbox": [ 149, 482, 158, 491 ], "score": 0.77, "content": "R", "type": "inline_equation" }, { "bbox": [ 158, 482, 198, 493 ], "score": 1.0, "content": ", capacity", "type": "text" }, { "bbox": [ 198, 482, 207, 491 ], "score": 0.79, "content": "C", "type": "inline_equation" }, { "bbox": [ 207, 482, 285, 493 ], "score": 1.0, "content": ", and time constant", "type": "text" }, { "bbox": [ 285, 481, 327, 491 ], "score": 0.91, "content": "\\tau = R \\cdot C", "type": "inline_equation" }, { "bbox": [ 328, 482, 506, 493 ], "score": 1.0, "content": "that relates to the group delay. Specifically,", "type": "text" } ], "index": 28 }, { "bbox": [ 106, 492, 506, 505 ], "spans": [ { "bbox": [ 106, 492, 506, 505 ], "score": 1.0, "content": "the contacts between the electrodes and the skin are predominantly capacitive whereas the skin itself", "type": "text" } ], "index": 29 }, { "bbox": [ 104, 502, 505, 517 ], "spans": [ { "bbox": [ 104, 502, 505, 517 ], "score": 1.0, "content": "is both resistive and capacitive (Kim et al., 2010). As explained in the previous part, an increasing", "type": "text" } ], "index": 30 }, { "bbox": [ 105, 513, 505, 527 ], "spans": [ { "bbox": [ 105, 513, 428, 527 ], "score": 1.0, "content": "distance between the attacker and the target electrode yields a larger resistance", "type": "text" }, { "bbox": [ 428, 515, 437, 524 ], "score": 0.57, "content": "R", "type": "inline_equation" }, { "bbox": [ 437, 513, 505, 527 ], "score": 1.0, "content": ". As a result, the", "type": "text" } ], "index": 31 }, { "bbox": [ 106, 525, 279, 537 ], "spans": [ { "bbox": [ 106, 525, 162, 537 ], "score": 1.0, "content": "time constant", "type": "text" }, { "bbox": [ 162, 527, 169, 535 ], "score": 0.76, "content": "\\tau", "type": "inline_equation" }, { "bbox": [ 170, 525, 279, 537 ], "score": 1.0, "content": "and the delay increase too.", "type": "text" } ], "index": 32 } ], "index": 28, "bbox_fs": [ 104, 437, 506, 537 ] }, { "type": "text", "bbox": [ 106, 541, 506, 619 ], "lines": [ { "bbox": [ 106, 542, 506, 554 ], "spans": [ { "bbox": [ 106, 542, 506, 554 ], "score": 1.0, "content": "Here, we model a linear distance-delay relation. We rely on a study by Plutchik & Hirsch (1963),", "type": "text" } ], "index": 33 }, { "bbox": [ 105, 552, 506, 566 ], "spans": [ { "bbox": [ 105, 552, 506, 566 ], "score": 1.0, "content": "which conducted human skin impedance and phase angle measurements by placing electrodes at", "type": "text" } ], "index": 34 }, { "bbox": [ 105, 564, 506, 577 ], "spans": [ { "bbox": [ 105, 564, 219, 577 ], "score": 1.0, "content": "an approximate distance of", "type": "text" }, { "bbox": [ 219, 564, 245, 574 ], "score": 0.68, "content": "1 0 \\mathrm { c m }", "type": "inline_equation" }, { "bbox": [ 245, 564, 457, 577 ], "score": 1.0, "content": "and applying voltages with frequencies in the range", "type": "text" }, { "bbox": [ 458, 564, 503, 574 ], "score": 0.78, "content": "2 { \\mathrm { - } } 1 0 0 0 \\mathrm { { H z } }", "type": "inline_equation" }, { "bbox": [ 504, 564, 506, 577 ], "score": 1.0, "content": ".", "type": "text" } ], "index": 35 }, { "bbox": [ 105, 575, 506, 588 ], "spans": [ { "bbox": [ 105, 575, 506, 588 ], "score": 1.0, "content": "When assuming a linear frequency-phase relation in low-frequency region (Qiao et al., 1994), one", "type": "text" } ], "index": 36 }, { "bbox": [ 106, 586, 505, 598 ], "spans": [ { "bbox": [ 106, 586, 234, 598 ], "score": 1.0, "content": "can derive the group delay to be", "type": "text" }, { "bbox": [ 234, 586, 261, 596 ], "score": 0.43, "content": "2 . 8 \\mathrm { m s }", "type": "inline_equation" }, { "bbox": [ 261, 586, 414, 598 ], "score": 1.0, "content": "when considering a measured angle of", "type": "text" }, { "bbox": [ 414, 586, 429, 596 ], "score": 0.88, "content": "1 0 ^ { \\circ }", "type": "inline_equation" }, { "bbox": [ 430, 586, 440, 598 ], "score": 1.0, "content": "at", "type": "text" }, { "bbox": [ 441, 586, 464, 596 ], "score": 0.72, "content": "1 0 \\mathrm { H z }", "type": "inline_equation" }, { "bbox": [ 465, 586, 505, 598 ], "score": 1.0, "content": ". As those", "type": "text" } ], "index": 37 }, { "bbox": [ 104, 596, 506, 611 ], "spans": [ { "bbox": [ 104, 596, 506, 611 ], "score": 1.0, "content": "measurements were conducted for only one distance, we extrapolate the delay for the remaining", "type": "text" } ], "index": 38 }, { "bbox": [ 106, 609, 267, 620 ], "spans": [ { "bbox": [ 106, 609, 267, 620 ], "score": 1.0, "content": "distances using a rectified linear model:", "type": "text" } ], "index": 39 } ], "index": 36, "bbox_fs": [ 104, 542, 506, 620 ] }, { "type": "interline_equation", "bbox": [ 171, 624, 438, 639 ], "lines": [ { "bbox": [ 171, 624, 438, 639 ], "spans": [ { "bbox": [ 171, 624, 438, 639 ], "score": 0.89, "content": "\\lambda _ { d } \\cdot ( l _ { i } - l _ { 0 } ) > 0 \\uparrow d ( l _ { i } , \\lambda _ { d } ) = \\lambda _ { d } \\cdot ( l _ { i } - l _ { 0 } ) + d _ { 0 } : d ( l _ { i } , \\lambda _ { d } ) = 0 ,", "type": "interline_equation", "image_path": "8c109f14baba8fdb3fa4ca00a590e185e17699666a46300fe54ae3c09558dc5d.jpg" } ] } ], "index": 40, "virtual_lines": [ { "bbox": [ 171, 624, 438, 639 ], "spans": [], "index": 40 } ] }, { "type": "text", "bbox": [ 106, 643, 506, 732 ], "lines": [ { "bbox": [ 105, 643, 507, 657 ], "spans": [ { "bbox": [ 105, 643, 133, 657 ], "score": 1.0, "content": "where", "type": "text" }, { "bbox": [ 133, 644, 183, 655 ], "score": 0.9, "content": "d _ { 0 } = 2 . 8 \\mathrm { m s }", "type": "inline_equation" }, { "bbox": [ 184, 643, 277, 657 ], "score": 1.0, "content": "is the delay at distance", "type": "text" }, { "bbox": [ 278, 644, 324, 655 ], "score": 0.93, "content": "l _ { 0 } = 1 0 \\mathrm { c m }", "type": "inline_equation" }, { "bbox": [ 324, 643, 507, 657 ], "score": 1.0, "content": ". The delay depends not only on the distance,", "type": "text" } ], "index": 41 }, { "bbox": [ 106, 655, 505, 667 ], "spans": [ { "bbox": [ 106, 655, 505, 667 ], "score": 1.0, "content": "but also on other parameters such as the electrode-to-skin contact, the humidity of the skin, etc. To", "type": "text" } ], "index": 42 }, { "bbox": [ 105, 665, 506, 678 ], "spans": [ { "bbox": [ 105, 665, 506, 678 ], "score": 1.0, "content": "this end, we evaluate the propagation of the attack with different characteristic delay parameters", "type": "text" } ], "index": 43 }, { "bbox": [ 106, 677, 506, 689 ], "spans": [ { "bbox": [ 106, 677, 192, 689 ], "score": 0.88, "content": "\\lambda _ { d } \\in \\left[ 0 . 1 , 0 . 5 6 3 \\right] \\mathrm { s / m }", "type": "inline_equation" }, { "bbox": [ 192, 677, 218, 689 ], "score": 1.0, "content": ". With", "type": "text" }, { "bbox": [ 218, 677, 256, 688 ], "score": 0.93, "content": "\\lambda _ { d } = 0 . 1", "type": "inline_equation" }, { "bbox": [ 257, 677, 506, 689 ], "score": 1.0, "content": "we cover the cases where very little delay happens, while the", "type": "text" } ], "index": 44 }, { "bbox": [ 105, 688, 506, 700 ], "spans": [ { "bbox": [ 105, 688, 183, 700 ], "score": 1.0, "content": "largest considered", "type": "text" }, { "bbox": [ 183, 688, 249, 699 ], "score": 0.9, "content": "\\lambda _ { d } = 0 . 5 6 3 \\mathrm { s / m }", "type": "inline_equation" }, { "bbox": [ 249, 688, 365, 700 ], "score": 1.0, "content": "yields a maximum delay of", "type": "text" }, { "bbox": [ 365, 688, 385, 698 ], "score": 0.34, "content": "0 . 1 \\mathrm { s }", "type": "inline_equation" }, { "bbox": [ 385, 688, 506, 700 ], "score": 1.0, "content": "at the farthest electrode T10,", "type": "text" } ], "index": 45 }, { "bbox": [ 104, 698, 507, 712 ], "spans": [ { "bbox": [ 104, 698, 507, 712 ], "score": 1.0, "content": "which is in alignment with the observed EEG measurements (Merlet et al., 2013; Sazgar & Young,", "type": "text" } ], "index": 46 }, { "bbox": [ 106, 710, 505, 722 ], "spans": [ { "bbox": [ 106, 710, 185, 722 ], "score": 1.0, "content": "2019). Similarly to", "type": "text" }, { "bbox": [ 185, 710, 199, 721 ], "score": 0.88, "content": "\\lambda _ { m }", "type": "inline_equation" }, { "bbox": [ 199, 710, 389, 722 ], "score": 1.0, "content": ", we showcase also for an intermediate value of", "type": "text" }, { "bbox": [ 389, 710, 427, 721 ], "score": 0.91, "content": "\\lambda _ { d } = 0 . 3", "type": "inline_equation" }, { "bbox": [ 427, 710, 505, 722 ], "score": 1.0, "content": "which corresponds", "type": "text" } ], "index": 47 }, { "bbox": [ 106, 721, 228, 733 ], "spans": [ { "bbox": [ 106, 721, 158, 733 ], "score": 1.0, "content": "to a delay of", "type": "text" }, { "bbox": [ 159, 721, 195, 731 ], "score": 0.27, "content": "0 . 0 5 3 \\mathrm { m s }", "type": "inline_equation" }, { "bbox": [ 196, 721, 228, 733 ], "score": 1.0, "content": "at T10.", "type": "text" } ], "index": 48 } ], "index": 44.5, "bbox_fs": [ 104, 643, 507, 733 ] } ] }, { "preproc_blocks": [ { "type": "text", "bbox": [ 97, 84, 501, 322 ], "lines": [ { "bbox": [ 106, 84, 347, 97 ], "spans": [ { "bbox": [ 106, 84, 347, 97 ], "score": 1.0, "content": "Algorithm 1: Generation of physiologically plausible UAP.", "type": "text" } ], "index": 0 }, { "bbox": [ 106, 99, 479, 111 ], "spans": [ { "bbox": [ 106, 99, 140, 111 ], "score": 1.0, "content": "input :", "type": "text" }, { "bbox": [ 141, 99, 169, 110 ], "score": 0.88, "content": "\\mathbf { X } _ { t r a i n }", "type": "inline_equation" }, { "bbox": [ 170, 99, 266, 111 ], "score": 1.0, "content": ", EEG training samples;", "type": "text" }, { "bbox": [ 266, 99, 296, 110 ], "score": 0.83, "content": "\\lambda _ { m } , \\lambda _ { d }", "type": "inline_equation" }, { "bbox": [ 296, 99, 427, 111 ], "score": 1.0, "content": ", spatial propagation parameters;", "type": "text" }, { "bbox": [ 427, 100, 434, 110 ], "score": 0.79, "content": "\\beta", "type": "inline_equation" }, { "bbox": [ 435, 99, 479, 111 ], "score": 1.0, "content": ", weight of", "type": "text" } ], "index": 1 }, { "bbox": [ 139, 109, 491, 122 ], "spans": [ { "bbox": [ 139, 109, 370, 122 ], "score": 1.0, "content": "derivative loss term; \u000f, maximum perturbation amplitude;", "type": "text" }, { "bbox": [ 370, 110, 379, 120 ], "score": 0.78, "content": "G", "type": "inline_equation" }, { "bbox": [ 380, 109, 491, 122 ], "score": 1.0, "content": ", number of PGD iterations;", "type": "text" } ], "index": 2 }, { "bbox": [ 141, 120, 227, 133 ], "spans": [ { "bbox": [ 141, 121, 149, 131 ], "score": 0.66, "content": "E", "type": "inline_equation" }, { "bbox": [ 150, 120, 227, 133 ], "score": 1.0, "content": ", number of epochs", "type": "text" } ], "index": 3 }, { "bbox": [ 106, 131, 249, 144 ], "spans": [ { "bbox": [ 106, 131, 249, 144 ], "score": 1.0, "content": "output :v, adversarial perturbation", "type": "text" } ], "index": 4 }, { "bbox": [ 96, 146, 490, 159 ], "spans": [ { "bbox": [ 96, 146, 106, 159 ], "score": 1.0, "content": "1", "type": "text" }, { "bbox": [ 106, 146, 197, 159 ], "score": 0.75, "content": "\\mathbf { v } \\mathcal { U } ( - \\epsilon , \\epsilon ) \\in \\mathbb { R } ^ { N _ { s } } ;", "type": "inline_equation" }, { "bbox": [ 198, 146, 200, 159 ], "score": 1.0, "content": ";", "type": "text" }, { "bbox": [ 395, 147, 490, 159 ], "score": 1.0, "content": "// Initialisation", "type": "text" } ], "index": 5 }, { "bbox": [ 97, 157, 182, 170 ], "spans": [ { "bbox": [ 97, 157, 121, 170 ], "score": 1.0, "content": "2 for", "type": "text" }, { "bbox": [ 122, 160, 148, 169 ], "score": 0.83, "content": "e \\gets 1", "type": "inline_equation" }, { "bbox": [ 148, 157, 159, 170 ], "score": 1.0, "content": "to", "type": "text" }, { "bbox": [ 160, 160, 168, 168 ], "score": 0.67, "content": "E", "type": "inline_equation" }, { "bbox": [ 168, 157, 182, 170 ], "score": 1.0, "content": "do", "type": "text" } ], "index": 6 }, { "bbox": [ 98, 167, 187, 183 ], "spans": [ { "bbox": [ 98, 172, 105, 180 ], "score": 1.0, "content": "3", "type": "text" }, { "bbox": [ 120, 167, 153, 183 ], "score": 1.0, "content": "Shuffle", "type": "text" }, { "bbox": [ 153, 169, 182, 181 ], "score": 0.89, "content": "\\mathbf { X } _ { t r a i n }", "type": "inline_equation" }, { "bbox": [ 182, 167, 187, 183 ], "score": 1.0, "content": ";", "type": "text" } ], "index": 7 }, { "bbox": [ 99, 178, 249, 193 ], "spans": [ { "bbox": [ 99, 183, 104, 190 ], "score": 1.0, "content": "4", "type": "text" }, { "bbox": [ 120, 178, 185, 193 ], "score": 1.0, "content": "for each batch", "type": "text" }, { "bbox": [ 185, 181, 235, 191 ], "score": 0.86, "content": "\\mathbf { B } \\in \\mathbf { X } _ { t r a i n }", "type": "inline_equation" }, { "bbox": [ 235, 178, 249, 193 ], "score": 1.0, "content": "do", "type": "text" } ], "index": 8 }, { "bbox": [ 99, 189, 173, 206 ], "spans": [ { "bbox": [ 99, 194, 105, 201 ], "score": 1.0, "content": "5", "type": "text" }, { "bbox": [ 137, 191, 167, 204 ], "score": 0.84, "content": "\\alpha \\frac \\epsilon 2", "type": "inline_equation" }, { "bbox": [ 167, 189, 173, 206 ], "score": 1.0, "content": ";", "type": "text" } ], "index": 9 }, { "bbox": [ 99, 202, 215, 215 ], "spans": [ { "bbox": [ 99, 207, 105, 214 ], "score": 1.0, "content": "6", "type": "text" }, { "bbox": [ 135, 202, 152, 215 ], "score": 1.0, "content": "for", "type": "text" }, { "bbox": [ 152, 204, 179, 214 ], "score": 0.8, "content": "g \\gets 1", "type": "inline_equation" }, { "bbox": [ 180, 202, 191, 215 ], "score": 1.0, "content": "to", "type": "text" }, { "bbox": [ 191, 204, 200, 213 ], "score": 0.67, "content": "G", "type": "inline_equation" }, { "bbox": [ 200, 202, 215, 215 ], "score": 1.0, "content": "do", "type": "text" } ], "index": 10 }, { "bbox": [ 99, 213, 491, 228 ], "spans": [ { "bbox": [ 99, 217, 105, 225 ], "score": 1.0, "content": "7", "type": "text" }, { "bbox": [ 151, 214, 235, 226 ], "score": 0.3, "content": "\\mathbf { \\bar { V } } H ( \\mathbf { v } , \\lambda _ { m } , \\lambda _ { d } ) ;", "type": "inline_equation" }, { "bbox": [ 366, 213, 491, 228 ], "score": 1.0, "content": "// Spatial propagation", "type": "text" } ], "index": 11 }, { "bbox": [ 99, 222, 491, 238 ], "spans": [ { "bbox": [ 99, 228, 106, 236 ], "score": 1.0, "content": "8", "type": "text" }, { "bbox": [ 150, 222, 243, 237 ], "score": 0.42, "content": "\\begin{array} { r } { \\dot { \\mathbf { p } } f ( H _ { b p } ( \\mathbf { B } + \\mathbf { V } ) ) } \\end{array}", "type": "inline_equation" }, { "bbox": [ 243, 224, 248, 237 ], "score": 1.0, "content": ";", "type": "text" }, { "bbox": [ 318, 224, 491, 238 ], "score": 1.0, "content": "// Model pass with perturbation", "type": "text" } ], "index": 12 }, { "bbox": [ 99, 237, 491, 257 ], "spans": [ { "bbox": [ 99, 244, 106, 252 ], "score": 1.0, "content": "9", "type": "text" }, { "bbox": [ 151, 237, 351, 257 ], "score": 0.37, "content": "\\begin{array} { r } { \\mathbf { v } \\mathbf { v } - \\alpha \\cdot \\mathrm { s i g n } ( \\nabla _ { \\mathbf { v } } ( l ( \\mathbf { p } , y _ { r e s t } ) - \\frac { \\beta } { \\epsilon } | | \\mathbf { v } ^ { \\prime } | | _ { 1 } ) ) } \\end{array}", "type": "inline_equation" }, { "bbox": [ 351, 237, 491, 256 ], "score": 1.0, "content": "; // Update w/derivative", "type": "text" } ], "index": 13 }, { "bbox": [ 96, 253, 490, 269 ], "spans": [ { "bbox": [ 96, 258, 106, 267 ], "score": 1.0, "content": "10", "type": "text" }, { "bbox": [ 150, 253, 216, 269 ], "score": 1.0, "content": "v ← clip\u000f (v);", "type": "text" }, { "bbox": [ 394, 256, 490, 268 ], "score": 1.0, "content": "// PGD projection", "type": "text" } ], "index": 14 }, { "bbox": [ 96, 267, 490, 287 ], "spans": [ { "bbox": [ 96, 273, 105, 283 ], "score": 1.0, "content": "11", "type": "text" }, { "bbox": [ 153, 268, 231, 284 ], "score": 0.48, "content": "\\alpha { \\frac { 0 . 1 - { \\frac { \\epsilon } { 2 } } } { G } } \\cdot g + { \\frac { \\epsilon } { 2 } }", "type": "inline_equation" }, { "bbox": [ 232, 267, 239, 287 ], "score": 1.0, "content": ";", "type": "text" }, { "bbox": [ 362, 271, 490, 283 ], "score": 1.0, "content": "// Learning rate update", "type": "text" } ], "index": 15 }, { "bbox": [ 95, 282, 157, 294 ], "spans": [ { "bbox": [ 95, 285, 105, 294 ], "score": 1.0, "content": "12", "type": "text" }, { "bbox": [ 135, 282, 157, 294 ], "score": 1.0, "content": "end", "type": "text" } ], "index": 16 }, { "bbox": [ 94, 294, 141, 308 ], "spans": [ { "bbox": [ 94, 296, 105, 308 ], "score": 1.0, "content": "13", "type": "text" }, { "bbox": [ 120, 294, 141, 307 ], "score": 1.0, "content": "end", "type": "text" } ], "index": 17 }, { "bbox": [ 92, 306, 126, 318 ], "spans": [ { "bbox": [ 92, 306, 126, 318 ], "score": 1.0, "content": "14 end", "type": "text" } ], "index": 18 } ], "index": 9 }, { "type": "title", "bbox": [ 107, 345, 203, 357 ], "lines": [ { "bbox": [ 106, 345, 203, 358 ], "spans": [ { "bbox": [ 106, 345, 203, 358 ], "score": 1.0, "content": "3.3 ATTACK DESIGN", "type": "text" } ], "index": 19 } ], "index": 19 }, { "type": "text", "bbox": [ 107, 366, 506, 410 ], "lines": [ { "bbox": [ 106, 366, 506, 378 ], "spans": [ { "bbox": [ 106, 366, 506, 378 ], "score": 1.0, "content": "We present practical DoS attacks in BCIs that respect domain constraints such as maximum amplitude,", "type": "text" } ], "index": 20 }, { "bbox": [ 105, 377, 506, 389 ], "spans": [ { "bbox": [ 105, 377, 506, 389 ], "score": 1.0, "content": "spectral distribution, physiological plausibility, and the spatial propagation of the perturbation. To this", "type": "text" } ], "index": 21 }, { "bbox": [ 105, 388, 505, 401 ], "spans": [ { "bbox": [ 105, 388, 505, 401 ], "score": 1.0, "content": "end, we formulate a general objective function that contains the spatial propagation, the preprocessing", "type": "text" } ], "index": 22 }, { "bbox": [ 106, 400, 284, 411 ], "spans": [ { "bbox": [ 106, 400, 284, 411 ], "score": 1.0, "content": "step, and the first order derivative loss term:", "type": "text" } ], "index": 23 } ], "index": 21.5 }, { "type": "interline_equation", "bbox": [ 163, 416, 447, 440 ], "lines": [ { "bbox": [ 163, 416, 447, 440 ], "spans": [ { "bbox": [ 163, 416, 447, 440 ], "score": 0.9, "content": "\\mathcal { L } _ { t o t } \\left( \\mathbf { X } , \\mathbf { v } , \\lambda _ { m } , \\lambda _ { d } \\right) = l \\left( H _ { b p } \\left( \\mathbf { X } + H ( \\mathbf { v } , \\lambda _ { m } , \\lambda _ { d } ) \\right) , y _ { r e s t } \\right) - \\frac { \\beta } { \\epsilon } | | \\mathbf { v } ^ { \\prime } | | _ { 1 } ,", "type": "interline_equation", "image_path": "fe16bc6e9801b89541c1b4b4d593a5597c8f9ad0f759f089214314544b703deb.jpg" } ] } ], "index": 24, "virtual_lines": [ { "bbox": [ 163, 416, 447, 440 ], "spans": [], "index": 24 } ] }, { "type": "text", "bbox": [ 107, 445, 505, 468 ], "lines": [ { "bbox": [ 106, 445, 506, 458 ], "spans": [ { "bbox": [ 106, 445, 134, 458 ], "score": 1.0, "content": "where", "type": "text" }, { "bbox": [ 134, 445, 156, 457 ], "score": 0.91, "content": "l ( \\cdot , \\cdot )", "type": "inline_equation" }, { "bbox": [ 156, 445, 409, 458 ], "score": 1.0, "content": "is the negative log-likelihood loss defined in equation 6 and", "type": "text" }, { "bbox": [ 410, 446, 441, 457 ], "score": 0.89, "content": "\\beta { = } 1 \\mathrm { e } { - } 6", "type": "inline_equation" }, { "bbox": [ 441, 445, 506, 458 ], "score": 1.0, "content": "is a scalar that", "type": "text" } ], "index": 25 }, { "bbox": [ 105, 456, 471, 468 ], "spans": [ { "bbox": [ 105, 456, 471, 468 ], "score": 1.0, "content": "weights the contribution of the derivative loss term. We compare different attack scenarios:", "type": "text" } ], "index": 26 } ], "index": 25.5 }, { "type": "text", "bbox": [ 106, 479, 506, 569 ], "lines": [ { "bbox": [ 105, 479, 506, 493 ], "spans": [ { "bbox": [ 105, 479, 506, 493 ], "score": 1.0, "content": "Instance-based attacks. A perturbation is computed using either FGSM or PGD based on the", "type": "text" } ], "index": 27 }, { "bbox": [ 105, 490, 506, 504 ], "spans": [ { "bbox": [ 105, 490, 309, 504 ], "score": 1.0, "content": "knowledge of the currently attacked EEG signal", "type": "text" }, { "bbox": [ 309, 491, 319, 501 ], "score": 0.27, "content": "\\mathbf { X }", "type": "inline_equation" }, { "bbox": [ 319, 490, 506, 504 ], "score": 1.0, "content": ". FGSM computes the perturbation as stated", "type": "text" } ], "index": 28 }, { "bbox": [ 105, 502, 504, 514 ], "spans": [ { "bbox": [ 105, 502, 210, 514 ], "score": 1.0, "content": "in equation 5, where the", "type": "text" }, { "bbox": [ 210, 504, 216, 512 ], "score": 0.65, "content": "\\epsilon", "type": "inline_equation" }, { "bbox": [ 217, 502, 466, 514 ], "score": 1.0, "content": "defines the perturbation amplitude which is varied between", "type": "text" }, { "bbox": [ 466, 502, 504, 513 ], "score": 0.82, "content": "1 { - } 5 0 \\mathrm { m V } .", "type": "inline_equation" } ], "index": 29 }, { "bbox": [ 105, 513, 506, 525 ], "spans": [ { "bbox": [ 105, 513, 351, 525 ], "score": 1.0, "content": "Alternatively, we compute the perturbation using PGD with", "type": "text" }, { "bbox": [ 351, 513, 376, 523 ], "score": 0.88, "content": "G { = } 1 0", "type": "inline_equation" }, { "bbox": [ 377, 513, 506, 525 ], "score": 1.0, "content": "iterations, where each iteration", "type": "text" } ], "index": 30 }, { "bbox": [ 106, 524, 504, 536 ], "spans": [ { "bbox": [ 106, 524, 416, 536 ], "score": 1.0, "content": "consists of a gradient-based update of the perturbation and a projection to the", "type": "text" }, { "bbox": [ 416, 524, 433, 535 ], "score": 0.9, "content": "L _ { \\infty }", "type": "inline_equation" }, { "bbox": [ 433, 524, 498, 536 ], "score": 1.0, "content": "ball with radius", "type": "text" }, { "bbox": [ 498, 526, 504, 534 ], "score": 0.66, "content": "\\epsilon", "type": "inline_equation" } ], "index": 31 }, { "bbox": [ 106, 534, 506, 547 ], "spans": [ { "bbox": [ 106, 535, 239, 547 ], "score": 1.0, "content": "(see equation 7). The update rate", "type": "text" }, { "bbox": [ 239, 537, 247, 545 ], "score": 0.8, "content": "\\alpha", "type": "inline_equation" }, { "bbox": [ 248, 535, 319, 547 ], "score": 1.0, "content": "is initialized with", "type": "text" }, { "bbox": [ 320, 534, 335, 547 ], "score": 0.88, "content": "\\epsilon / 2", "type": "inline_equation" }, { "bbox": [ 335, 535, 506, 547 ], "score": 1.0, "content": "and linearly decreased with each iteration,", "type": "text" } ], "index": 32 }, { "bbox": [ 105, 546, 506, 558 ], "spans": [ { "bbox": [ 105, 546, 209, 558 ], "score": 1.0, "content": "reaching a final value of", "type": "text" }, { "bbox": [ 209, 546, 241, 556 ], "score": 0.78, "content": "0 . 1 \\mathrm { m V }", "type": "inline_equation" }, { "bbox": [ 241, 546, 506, 558 ], "score": 1.0, "content": "at iteration 10. The PGD computation is restarted 5 times with", "type": "text" } ], "index": 33 }, { "bbox": [ 105, 556, 507, 570 ], "spans": [ { "bbox": [ 105, 556, 468, 570 ], "score": 1.0, "content": "different initial perturbations, which are drawn from a uniform distribution within the range", "type": "text" }, { "bbox": [ 469, 556, 503, 569 ], "score": 0.92, "content": "[ - \\epsilon , + \\epsilon ]", "type": "inline_equation" }, { "bbox": [ 503, 556, 507, 570 ], "score": 1.0, "content": ".", "type": "text" } ], "index": 34 } ], "index": 30.5 }, { "type": "text", "bbox": [ 106, 580, 504, 603 ], "lines": [ { "bbox": [ 106, 580, 505, 593 ], "spans": [ { "bbox": [ 106, 580, 505, 593 ], "score": 1.0, "content": "Universal attacks. A universal perturbation is computed for all the samples in the training data.", "type": "text" } ], "index": 35 }, { "bbox": [ 106, 591, 270, 604 ], "spans": [ { "bbox": [ 106, 591, 270, 604 ], "score": 1.0, "content": "We optimize the UAP objective function", "type": "text" } ], "index": 36 } ], "index": 35.5 }, { "type": "interline_equation", "bbox": [ 207, 608, 404, 626 ], "lines": [ { "bbox": [ 207, 608, 404, 626 ], "spans": [ { "bbox": [ 207, 608, 404, 626 ], "score": 0.9, "content": "\\operatorname* { m i n } _ { \\mathbf { v } } E _ { \\mathbf { X } \\sim D } \\mathcal { L } _ { t o t } \\left( \\mathbf { X } , \\mathbf { v } , \\lambda _ { m } , \\lambda _ { d } \\right) \\quad \\mathrm { ~ s . t . ~ } | | \\mathbf { v } | | _ { \\infty } \\leq \\epsilon", "type": "interline_equation", "image_path": "abca44dd6ab1e65427156e0254551c8079b96a3d17410266c428125a6fa67a74.jpg" } ] } ], "index": 37, "virtual_lines": [ { "bbox": [ 207, 608, 404, 626 ], "spans": [], "index": 37 } ] }, { "type": "text", "bbox": [ 106, 631, 505, 676 ], "lines": [ { "bbox": [ 106, 632, 505, 644 ], "spans": [ { "bbox": [ 106, 632, 505, 644 ], "score": 1.0, "content": "using batched PGD. We pass a batch of 16 samples together with the current perturbation through", "type": "text" } ], "index": 38 }, { "bbox": [ 105, 641, 506, 655 ], "spans": [ { "bbox": [ 105, 641, 506, 655 ], "score": 1.0, "content": "the preprocessing and classifier, compute the loss function, and update the perturbation based on", "type": "text" } ], "index": 39 }, { "bbox": [ 105, 653, 506, 666 ], "spans": [ { "bbox": [ 105, 653, 329, 666 ], "score": 1.0, "content": "the negative gradient with consecutive projection to the", "type": "text" }, { "bbox": [ 329, 653, 345, 664 ], "score": 0.92, "content": "L _ { \\infty }", "type": "inline_equation" }, { "bbox": [ 345, 653, 411, 666 ], "score": 1.0, "content": "ball with radius", "type": "text" }, { "bbox": [ 411, 655, 417, 663 ], "score": 0.48, "content": "\\epsilon", "type": "inline_equation" }, { "bbox": [ 417, 653, 506, 666 ], "score": 1.0, "content": ". This step is repeated", "type": "text" } ], "index": 40 }, { "bbox": [ 107, 663, 475, 676 ], "spans": [ { "bbox": [ 107, 664, 131, 675 ], "score": 0.86, "content": "G { = } 1 0", "type": "inline_equation" }, { "bbox": [ 132, 663, 417, 676 ], "score": 1.0, "content": "times before processing the next batch. Overall, the UAP is learned for", "type": "text" }, { "bbox": [ 417, 664, 442, 674 ], "score": 0.88, "content": "E { = } 1 0", "type": "inline_equation" }, { "bbox": [ 443, 663, 475, 676 ], "score": 1.0, "content": "epochs.", "type": "text" } ], "index": 41 } ], "index": 39.5 }, { "type": "text", "bbox": [ 107, 687, 505, 732 ], "lines": [ { "bbox": [ 105, 687, 506, 700 ], "spans": [ { "bbox": [ 105, 687, 506, 700 ], "score": 1.0, "content": "Propagation model. We distinguish between three use cases of spatial propagation model, where", "type": "text" } ], "index": 42 }, { "bbox": [ 105, 698, 506, 712 ], "spans": [ { "bbox": [ 105, 698, 506, 712 ], "score": 1.0, "content": "in all cases either an instance-specific attack or a universal attack can be computed: Case 1) Ignore", "type": "text" } ], "index": 43 }, { "bbox": [ 105, 709, 506, 722 ], "spans": [ { "bbox": [ 105, 709, 324, 722 ], "score": 1.0, "content": "the propagation model: a multi-channel perturbation", "type": "text" }, { "bbox": [ 324, 710, 334, 720 ], "score": 0.69, "content": "\\mathbf { V }", "type": "inline_equation" }, { "bbox": [ 335, 709, 506, 722 ], "score": 1.0, "content": "is computed, which attacks each channel", "type": "text" } ], "index": 44 }, { "bbox": [ 106, 721, 506, 733 ], "spans": [ { "bbox": [ 106, 721, 235, 733 ], "score": 1.0, "content": "individually, replacing the terms", "type": "text" }, { "bbox": [ 235, 721, 292, 732 ], "score": 0.93, "content": "H ( \\mathbf { v } , \\lambda _ { m } , \\lambda _ { d } )", "type": "inline_equation" }, { "bbox": [ 292, 721, 306, 733 ], "score": 1.0, "content": "by", "type": "text" }, { "bbox": [ 306, 721, 316, 731 ], "score": 0.73, "content": "\\mathbf { V }", "type": "inline_equation" }, { "bbox": [ 316, 721, 333, 733 ], "score": 1.0, "content": "and", "type": "text" }, { "bbox": [ 334, 721, 344, 731 ], "score": 0.86, "content": "\\mathbf { v } ^ { \\prime }", "type": "inline_equation" }, { "bbox": [ 344, 721, 357, 733 ], "score": 1.0, "content": "by", "type": "text" }, { "bbox": [ 357, 721, 370, 731 ], "score": 0.85, "content": "\\mathbf { V } ^ { \\prime }", "type": "inline_equation" }, { "bbox": [ 370, 721, 506, 733 ], "score": 1.0, "content": "in equation 15. Case 2) Consider", "type": "text" } ], "index": 45 } ], "index": 43.5 } ], "page_idx": 5, "page_size": [ 612, 792 ], "discarded_blocks": [ { "type": "discarded", "bbox": [ 107, 27, 308, 37 ], "lines": [ { "bbox": [ 107, 26, 308, 38 ], "spans": [ { "bbox": [ 107, 26, 308, 38 ], "score": 1.0, "content": "Under review as a conference paper at ICLR 2022", "type": "text" } ] } ] }, { "type": "discarded", "bbox": [ 302, 751, 309, 760 ], "lines": [ { "bbox": [ 302, 750, 310, 761 ], "spans": [ { "bbox": [ 302, 750, 310, 761 ], "score": 1.0, "content": "6", "type": "text" } ] } ] } ], "para_blocks": [ { "type": "text", "bbox": [ 97, 84, 501, 322 ], "lines": [ { "bbox": [ 106, 84, 347, 97 ], "spans": [ { "bbox": [ 106, 84, 347, 97 ], "score": 1.0, "content": "Algorithm 1: Generation of physiologically plausible UAP.", "type": "text" } ], "index": 0 }, { "bbox": [ 106, 99, 479, 111 ], "spans": [ { "bbox": [ 106, 99, 140, 111 ], "score": 1.0, "content": "input :", "type": "text" }, { "bbox": [ 141, 99, 169, 110 ], "score": 0.88, "content": "\\mathbf { X } _ { t r a i n }", "type": "inline_equation" }, { "bbox": [ 170, 99, 266, 111 ], "score": 1.0, "content": ", EEG training samples;", "type": "text" }, { "bbox": [ 266, 99, 296, 110 ], "score": 0.83, "content": "\\lambda _ { m } , \\lambda _ { d }", "type": "inline_equation" }, { "bbox": [ 296, 99, 427, 111 ], "score": 1.0, "content": ", spatial propagation parameters;", "type": "text" }, { "bbox": [ 427, 100, 434, 110 ], "score": 0.79, "content": "\\beta", "type": "inline_equation" }, { "bbox": [ 435, 99, 479, 111 ], "score": 1.0, "content": ", weight of", "type": "text" } ], "index": 1 }, { "bbox": [ 139, 109, 491, 122 ], "spans": [ { "bbox": [ 139, 109, 370, 122 ], "score": 1.0, "content": "derivative loss term; \u000f, maximum perturbation amplitude;", "type": "text" }, { "bbox": [ 370, 110, 379, 120 ], "score": 0.78, "content": "G", "type": "inline_equation" }, { "bbox": [ 380, 109, 491, 122 ], "score": 1.0, "content": ", number of PGD iterations;", "type": "text" } ], "index": 2 }, { "bbox": [ 141, 120, 227, 133 ], "spans": [ { "bbox": [ 141, 121, 149, 131 ], "score": 0.66, "content": "E", "type": "inline_equation" }, { "bbox": [ 150, 120, 227, 133 ], "score": 1.0, "content": ", number of epochs", "type": "text" } ], "index": 3 }, { "bbox": [ 106, 131, 249, 144 ], "spans": [ { "bbox": [ 106, 131, 249, 144 ], "score": 1.0, "content": "output :v, adversarial perturbation", "type": "text" } ], "index": 4 }, { "bbox": [ 96, 146, 490, 159 ], "spans": [ { "bbox": [ 96, 146, 106, 159 ], "score": 1.0, "content": "1", "type": "text" }, { "bbox": [ 106, 146, 197, 159 ], "score": 0.75, "content": "\\mathbf { v } \\mathcal { U } ( - \\epsilon , \\epsilon ) \\in \\mathbb { R } ^ { N _ { s } } ;", "type": "inline_equation" }, { "bbox": [ 198, 146, 200, 159 ], "score": 1.0, "content": ";", "type": "text" }, { "bbox": [ 395, 147, 490, 159 ], "score": 1.0, "content": "// Initialisation", "type": "text" } ], "index": 5 }, { "bbox": [ 97, 157, 182, 170 ], "spans": [ { "bbox": [ 97, 157, 121, 170 ], "score": 1.0, "content": "2 for", "type": "text" }, { "bbox": [ 122, 160, 148, 169 ], "score": 0.83, "content": "e \\gets 1", "type": "inline_equation" }, { "bbox": [ 148, 157, 159, 170 ], "score": 1.0, "content": "to", "type": "text" }, { "bbox": [ 160, 160, 168, 168 ], "score": 0.67, "content": "E", "type": "inline_equation" }, { "bbox": [ 168, 157, 182, 170 ], "score": 1.0, "content": "do", "type": "text" } ], "index": 6 }, { "bbox": [ 98, 167, 187, 183 ], "spans": [ { "bbox": [ 98, 172, 105, 180 ], "score": 1.0, "content": "3", "type": "text" }, { "bbox": [ 120, 167, 153, 183 ], "score": 1.0, "content": "Shuffle", "type": "text" }, { "bbox": [ 153, 169, 182, 181 ], "score": 0.89, "content": "\\mathbf { X } _ { t r a i n }", "type": "inline_equation" }, { "bbox": [ 182, 167, 187, 183 ], "score": 1.0, "content": ";", "type": "text" } ], "index": 7 }, { "bbox": [ 99, 178, 249, 193 ], "spans": [ { "bbox": [ 99, 183, 104, 190 ], "score": 1.0, "content": "4", "type": "text" }, { "bbox": [ 120, 178, 185, 193 ], "score": 1.0, "content": "for each batch", "type": "text" }, { "bbox": [ 185, 181, 235, 191 ], "score": 0.86, "content": "\\mathbf { B } \\in \\mathbf { X } _ { t r a i n }", "type": "inline_equation" }, { "bbox": [ 235, 178, 249, 193 ], "score": 1.0, "content": "do", "type": "text" } ], "index": 8 }, { "bbox": [ 99, 189, 173, 206 ], "spans": [ { "bbox": [ 99, 194, 105, 201 ], "score": 1.0, "content": "5", "type": "text" }, { "bbox": [ 137, 191, 167, 204 ], "score": 0.84, "content": "\\alpha \\frac \\epsilon 2", "type": "inline_equation" }, { "bbox": [ 167, 189, 173, 206 ], "score": 1.0, "content": ";", "type": "text" } ], "index": 9 }, { "bbox": [ 99, 202, 215, 215 ], "spans": [ { "bbox": [ 99, 207, 105, 214 ], "score": 1.0, "content": "6", "type": "text" }, { "bbox": [ 135, 202, 152, 215 ], "score": 1.0, "content": "for", "type": "text" }, { "bbox": [ 152, 204, 179, 214 ], "score": 0.8, "content": "g \\gets 1", "type": "inline_equation" }, { "bbox": [ 180, 202, 191, 215 ], "score": 1.0, "content": "to", "type": "text" }, { "bbox": [ 191, 204, 200, 213 ], "score": 0.67, "content": "G", "type": "inline_equation" }, { "bbox": [ 200, 202, 215, 215 ], "score": 1.0, "content": "do", "type": "text" } ], "index": 10 }, { "bbox": [ 99, 213, 491, 228 ], "spans": [ { "bbox": [ 99, 217, 105, 225 ], "score": 1.0, "content": "7", "type": "text" }, { "bbox": [ 151, 214, 235, 226 ], "score": 0.3, "content": "\\mathbf { \\bar { V } } H ( \\mathbf { v } , \\lambda _ { m } , \\lambda _ { d } ) ;", "type": "inline_equation" }, { "bbox": [ 366, 213, 491, 228 ], "score": 1.0, "content": "// Spatial propagation", "type": "text" } ], "index": 11 }, { "bbox": [ 99, 222, 491, 238 ], "spans": [ { "bbox": [ 99, 228, 106, 236 ], "score": 1.0, "content": "8", "type": "text" }, { "bbox": [ 150, 222, 243, 237 ], "score": 0.42, "content": "\\begin{array} { r } { \\dot { \\mathbf { p } } f ( H _ { b p } ( \\mathbf { B } + \\mathbf { V } ) ) } \\end{array}", "type": "inline_equation" }, { "bbox": [ 243, 224, 248, 237 ], "score": 1.0, "content": ";", "type": "text" }, { "bbox": [ 318, 224, 491, 238 ], "score": 1.0, "content": "// Model pass with perturbation", "type": "text" } ], "index": 12 }, { "bbox": [ 99, 237, 491, 257 ], "spans": [ { "bbox": [ 99, 244, 106, 252 ], "score": 1.0, "content": "9", "type": "text" }, { "bbox": [ 151, 237, 351, 257 ], "score": 0.37, "content": "\\begin{array} { r } { \\mathbf { v } \\mathbf { v } - \\alpha \\cdot \\mathrm { s i g n } ( \\nabla _ { \\mathbf { v } } ( l ( \\mathbf { p } , y _ { r e s t } ) - \\frac { \\beta } { \\epsilon } | | \\mathbf { v } ^ { \\prime } | | _ { 1 } ) ) } \\end{array}", "type": "inline_equation" }, { "bbox": [ 351, 237, 491, 256 ], "score": 1.0, "content": "; // Update w/derivative", "type": "text" } ], "index": 13 }, { "bbox": [ 96, 253, 490, 269 ], "spans": [ { "bbox": [ 96, 258, 106, 267 ], "score": 1.0, "content": "10", "type": "text" }, { "bbox": [ 150, 253, 216, 269 ], "score": 1.0, "content": "v ← clip\u000f (v);", "type": "text" }, { "bbox": [ 394, 256, 490, 268 ], "score": 1.0, "content": "// PGD projection", "type": "text" } ], "index": 14 }, { "bbox": [ 96, 267, 490, 287 ], "spans": [ { "bbox": [ 96, 273, 105, 283 ], "score": 1.0, "content": "11", "type": "text" }, { "bbox": [ 153, 268, 231, 284 ], "score": 0.48, "content": "\\alpha { \\frac { 0 . 1 - { \\frac { \\epsilon } { 2 } } } { G } } \\cdot g + { \\frac { \\epsilon } { 2 } }", "type": "inline_equation" }, { "bbox": [ 232, 267, 239, 287 ], "score": 1.0, "content": ";", "type": "text" }, { "bbox": [ 362, 271, 490, 283 ], "score": 1.0, "content": "// Learning rate update", "type": "text" } ], "index": 15 }, { "bbox": [ 95, 282, 157, 294 ], "spans": [ { "bbox": [ 95, 285, 105, 294 ], "score": 1.0, "content": "12", "type": "text" }, { "bbox": [ 135, 282, 157, 294 ], "score": 1.0, "content": "end", "type": "text" } ], "index": 16 }, { "bbox": [ 94, 294, 141, 308 ], "spans": [ { "bbox": [ 94, 296, 105, 308 ], "score": 1.0, "content": "13", "type": "text" }, { "bbox": [ 120, 294, 141, 307 ], "score": 1.0, "content": "end", "type": "text" } ], "index": 17 }, { "bbox": [ 92, 306, 126, 318 ], "spans": [ { "bbox": [ 92, 306, 126, 318 ], "score": 1.0, "content": "14 end", "type": "text" } ], "index": 18 } ], "index": 9, "bbox_fs": [ 92, 84, 491, 318 ] }, { "type": "title", "bbox": [ 107, 345, 203, 357 ], "lines": [ { "bbox": [ 106, 345, 203, 358 ], "spans": [ { "bbox": [ 106, 345, 203, 358 ], "score": 1.0, "content": "3.3 ATTACK DESIGN", "type": "text" } ], "index": 19 } ], "index": 19 }, { "type": "text", "bbox": [ 107, 366, 506, 410 ], "lines": [ { "bbox": [ 106, 366, 506, 378 ], "spans": [ { "bbox": [ 106, 366, 506, 378 ], "score": 1.0, "content": "We present practical DoS attacks in BCIs that respect domain constraints such as maximum amplitude,", "type": "text" } ], "index": 20 }, { "bbox": [ 105, 377, 506, 389 ], "spans": [ { "bbox": [ 105, 377, 506, 389 ], "score": 1.0, "content": "spectral distribution, physiological plausibility, and the spatial propagation of the perturbation. To this", "type": "text" } ], "index": 21 }, { "bbox": [ 105, 388, 505, 401 ], "spans": [ { "bbox": [ 105, 388, 505, 401 ], "score": 1.0, "content": "end, we formulate a general objective function that contains the spatial propagation, the preprocessing", "type": "text" } ], "index": 22 }, { "bbox": [ 106, 400, 284, 411 ], "spans": [ { "bbox": [ 106, 400, 284, 411 ], "score": 1.0, "content": "step, and the first order derivative loss term:", "type": "text" } ], "index": 23 } ], "index": 21.5, "bbox_fs": [ 105, 366, 506, 411 ] }, { "type": "interline_equation", "bbox": [ 163, 416, 447, 440 ], "lines": [ { "bbox": [ 163, 416, 447, 440 ], "spans": [ { "bbox": [ 163, 416, 447, 440 ], "score": 0.9, "content": "\\mathcal { L } _ { t o t } \\left( \\mathbf { X } , \\mathbf { v } , \\lambda _ { m } , \\lambda _ { d } \\right) = l \\left( H _ { b p } \\left( \\mathbf { X } + H ( \\mathbf { v } , \\lambda _ { m } , \\lambda _ { d } ) \\right) , y _ { r e s t } \\right) - \\frac { \\beta } { \\epsilon } | | \\mathbf { v } ^ { \\prime } | | _ { 1 } ,", "type": "interline_equation", "image_path": "fe16bc6e9801b89541c1b4b4d593a5597c8f9ad0f759f089214314544b703deb.jpg" } ] } ], "index": 24, "virtual_lines": [ { "bbox": [ 163, 416, 447, 440 ], "spans": [], "index": 24 } ] }, { "type": "text", "bbox": [ 107, 445, 505, 468 ], "lines": [ { "bbox": [ 106, 445, 506, 458 ], "spans": [ { "bbox": [ 106, 445, 134, 458 ], "score": 1.0, "content": "where", "type": "text" }, { "bbox": [ 134, 445, 156, 457 ], "score": 0.91, "content": "l ( \\cdot , \\cdot )", "type": "inline_equation" }, { "bbox": [ 156, 445, 409, 458 ], "score": 1.0, "content": "is the negative log-likelihood loss defined in equation 6 and", "type": "text" }, { "bbox": [ 410, 446, 441, 457 ], "score": 0.89, "content": "\\beta { = } 1 \\mathrm { e } { - } 6", "type": "inline_equation" }, { "bbox": [ 441, 445, 506, 458 ], "score": 1.0, "content": "is a scalar that", "type": "text" } ], "index": 25 }, { "bbox": [ 105, 456, 471, 468 ], "spans": [ { "bbox": [ 105, 456, 471, 468 ], "score": 1.0, "content": "weights the contribution of the derivative loss term. We compare different attack scenarios:", "type": "text" } ], "index": 26 } ], "index": 25.5, "bbox_fs": [ 105, 445, 506, 468 ] }, { "type": "text", "bbox": [ 106, 479, 506, 569 ], "lines": [ { "bbox": [ 105, 479, 506, 493 ], "spans": [ { "bbox": [ 105, 479, 506, 493 ], "score": 1.0, "content": "Instance-based attacks. A perturbation is computed using either FGSM or PGD based on the", "type": "text" } ], "index": 27 }, { "bbox": [ 105, 490, 506, 504 ], "spans": [ { "bbox": [ 105, 490, 309, 504 ], "score": 1.0, "content": "knowledge of the currently attacked EEG signal", "type": "text" }, { "bbox": [ 309, 491, 319, 501 ], "score": 0.27, "content": "\\mathbf { X }", "type": "inline_equation" }, { "bbox": [ 319, 490, 506, 504 ], "score": 1.0, "content": ". FGSM computes the perturbation as stated", "type": "text" } ], "index": 28 }, { "bbox": [ 105, 502, 504, 514 ], "spans": [ { "bbox": [ 105, 502, 210, 514 ], "score": 1.0, "content": "in equation 5, where the", "type": "text" }, { "bbox": [ 210, 504, 216, 512 ], "score": 0.65, "content": "\\epsilon", "type": "inline_equation" }, { "bbox": [ 217, 502, 466, 514 ], "score": 1.0, "content": "defines the perturbation amplitude which is varied between", "type": "text" }, { "bbox": [ 466, 502, 504, 513 ], "score": 0.82, "content": "1 { - } 5 0 \\mathrm { m V } .", "type": "inline_equation" } ], "index": 29 }, { "bbox": [ 105, 513, 506, 525 ], "spans": [ { "bbox": [ 105, 513, 351, 525 ], "score": 1.0, "content": "Alternatively, we compute the perturbation using PGD with", "type": "text" }, { "bbox": [ 351, 513, 376, 523 ], "score": 0.88, "content": "G { = } 1 0", "type": "inline_equation" }, { "bbox": [ 377, 513, 506, 525 ], "score": 1.0, "content": "iterations, where each iteration", "type": "text" } ], "index": 30 }, { "bbox": [ 106, 524, 504, 536 ], "spans": [ { "bbox": [ 106, 524, 416, 536 ], "score": 1.0, "content": "consists of a gradient-based update of the perturbation and a projection to the", "type": "text" }, { "bbox": [ 416, 524, 433, 535 ], "score": 0.9, "content": "L _ { \\infty }", "type": "inline_equation" }, { "bbox": [ 433, 524, 498, 536 ], "score": 1.0, "content": "ball with radius", "type": "text" }, { "bbox": [ 498, 526, 504, 534 ], "score": 0.66, "content": "\\epsilon", "type": "inline_equation" } ], "index": 31 }, { "bbox": [ 106, 534, 506, 547 ], "spans": [ { "bbox": [ 106, 535, 239, 547 ], "score": 1.0, "content": "(see equation 7). The update rate", "type": "text" }, { "bbox": [ 239, 537, 247, 545 ], "score": 0.8, "content": "\\alpha", "type": "inline_equation" }, { "bbox": [ 248, 535, 319, 547 ], "score": 1.0, "content": "is initialized with", "type": "text" }, { "bbox": [ 320, 534, 335, 547 ], "score": 0.88, "content": "\\epsilon / 2", "type": "inline_equation" }, { "bbox": [ 335, 535, 506, 547 ], "score": 1.0, "content": "and linearly decreased with each iteration,", "type": "text" } ], "index": 32 }, { "bbox": [ 105, 546, 506, 558 ], "spans": [ { "bbox": [ 105, 546, 209, 558 ], "score": 1.0, "content": "reaching a final value of", "type": "text" }, { "bbox": [ 209, 546, 241, 556 ], "score": 0.78, "content": "0 . 1 \\mathrm { m V }", "type": "inline_equation" }, { "bbox": [ 241, 546, 506, 558 ], "score": 1.0, "content": "at iteration 10. The PGD computation is restarted 5 times with", "type": "text" } ], "index": 33 }, { "bbox": [ 105, 556, 507, 570 ], "spans": [ { "bbox": [ 105, 556, 468, 570 ], "score": 1.0, "content": "different initial perturbations, which are drawn from a uniform distribution within the range", "type": "text" }, { "bbox": [ 469, 556, 503, 569 ], "score": 0.92, "content": "[ - \\epsilon , + \\epsilon ]", "type": "inline_equation" }, { "bbox": [ 503, 556, 507, 570 ], "score": 1.0, "content": ".", "type": "text" } ], "index": 34 } ], "index": 30.5, "bbox_fs": [ 105, 479, 507, 570 ] }, { "type": "text", "bbox": [ 106, 580, 504, 603 ], "lines": [ { "bbox": [ 106, 580, 505, 593 ], "spans": [ { "bbox": [ 106, 580, 505, 593 ], "score": 1.0, "content": "Universal attacks. A universal perturbation is computed for all the samples in the training data.", "type": "text" } ], "index": 35 }, { "bbox": [ 106, 591, 270, 604 ], "spans": [ { "bbox": [ 106, 591, 270, 604 ], "score": 1.0, "content": "We optimize the UAP objective function", "type": "text" } ], "index": 36 } ], "index": 35.5, "bbox_fs": [ 106, 580, 505, 604 ] }, { "type": "interline_equation", "bbox": [ 207, 608, 404, 626 ], "lines": [ { "bbox": [ 207, 608, 404, 626 ], "spans": [ { "bbox": [ 207, 608, 404, 626 ], "score": 0.9, "content": "\\operatorname* { m i n } _ { \\mathbf { v } } E _ { \\mathbf { X } \\sim D } \\mathcal { L } _ { t o t } \\left( \\mathbf { X } , \\mathbf { v } , \\lambda _ { m } , \\lambda _ { d } \\right) \\quad \\mathrm { ~ s . t . ~ } | | \\mathbf { v } | | _ { \\infty } \\leq \\epsilon", "type": "interline_equation", "image_path": "abca44dd6ab1e65427156e0254551c8079b96a3d17410266c428125a6fa67a74.jpg" } ] } ], "index": 37, "virtual_lines": [ { "bbox": [ 207, 608, 404, 626 ], "spans": [], "index": 37 } ] }, { "type": "text", "bbox": [ 106, 631, 505, 676 ], "lines": [ { "bbox": [ 106, 632, 505, 644 ], "spans": [ { "bbox": [ 106, 632, 505, 644 ], "score": 1.0, "content": "using batched PGD. We pass a batch of 16 samples together with the current perturbation through", "type": "text" } ], "index": 38 }, { "bbox": [ 105, 641, 506, 655 ], "spans": [ { "bbox": [ 105, 641, 506, 655 ], "score": 1.0, "content": "the preprocessing and classifier, compute the loss function, and update the perturbation based on", "type": "text" } ], "index": 39 }, { "bbox": [ 105, 653, 506, 666 ], "spans": [ { "bbox": [ 105, 653, 329, 666 ], "score": 1.0, "content": "the negative gradient with consecutive projection to the", "type": "text" }, { "bbox": [ 329, 653, 345, 664 ], "score": 0.92, "content": "L _ { \\infty }", "type": "inline_equation" }, { "bbox": [ 345, 653, 411, 666 ], "score": 1.0, "content": "ball with radius", "type": "text" }, { "bbox": [ 411, 655, 417, 663 ], "score": 0.48, "content": "\\epsilon", "type": "inline_equation" }, { "bbox": [ 417, 653, 506, 666 ], "score": 1.0, "content": ". This step is repeated", "type": "text" } ], "index": 40 }, { "bbox": [ 107, 663, 475, 676 ], "spans": [ { "bbox": [ 107, 664, 131, 675 ], "score": 0.86, "content": "G { = } 1 0", "type": "inline_equation" }, { "bbox": [ 132, 663, 417, 676 ], "score": 1.0, "content": "times before processing the next batch. Overall, the UAP is learned for", "type": "text" }, { "bbox": [ 417, 664, 442, 674 ], "score": 0.88, "content": "E { = } 1 0", "type": "inline_equation" }, { "bbox": [ 443, 663, 475, 676 ], "score": 1.0, "content": "epochs.", "type": "text" } ], "index": 41 } ], "index": 39.5, "bbox_fs": [ 105, 632, 506, 676 ] }, { "type": "text", "bbox": [ 107, 687, 505, 732 ], "lines": [ { "bbox": [ 105, 687, 506, 700 ], "spans": [ { "bbox": [ 105, 687, 506, 700 ], "score": 1.0, "content": "Propagation model. We distinguish between three use cases of spatial propagation model, where", "type": "text" } ], "index": 42 }, { "bbox": [ 105, 698, 506, 712 ], "spans": [ { "bbox": [ 105, 698, 506, 712 ], "score": 1.0, "content": "in all cases either an instance-specific attack or a universal attack can be computed: Case 1) Ignore", "type": "text" } ], "index": 43 }, { "bbox": [ 105, 709, 506, 722 ], "spans": [ { "bbox": [ 105, 709, 324, 722 ], "score": 1.0, "content": "the propagation model: a multi-channel perturbation", "type": "text" }, { "bbox": [ 324, 710, 334, 720 ], "score": 0.69, "content": "\\mathbf { V }", "type": "inline_equation" }, { "bbox": [ 335, 709, 506, 722 ], "score": 1.0, "content": "is computed, which attacks each channel", "type": "text" } ], "index": 44 }, { "bbox": [ 106, 721, 506, 733 ], "spans": [ { "bbox": [ 106, 721, 235, 733 ], "score": 1.0, "content": "individually, replacing the terms", "type": "text" }, { "bbox": [ 235, 721, 292, 732 ], "score": 0.93, "content": "H ( \\mathbf { v } , \\lambda _ { m } , \\lambda _ { d } )", "type": "inline_equation" }, { "bbox": [ 292, 721, 306, 733 ], "score": 1.0, "content": "by", "type": "text" }, { "bbox": [ 306, 721, 316, 731 ], "score": 0.73, "content": "\\mathbf { V }", "type": "inline_equation" }, { "bbox": [ 316, 721, 333, 733 ], "score": 1.0, "content": "and", "type": "text" }, { "bbox": [ 334, 721, 344, 731 ], "score": 0.86, "content": "\\mathbf { v } ^ { \\prime }", "type": "inline_equation" }, { "bbox": [ 344, 721, 357, 733 ], "score": 1.0, "content": "by", "type": "text" }, { "bbox": [ 357, 721, 370, 731 ], "score": 0.85, "content": "\\mathbf { V } ^ { \\prime }", "type": "inline_equation" }, { "bbox": [ 370, 721, 506, 733 ], "score": 1.0, "content": "in equation 15. Case 2) Consider", "type": "text" } ], "index": 45 }, { "bbox": [ 105, 377, 505, 390 ], "spans": [ { "bbox": [ 105, 377, 505, 390 ], "score": 1.0, "content": "the propagation model: a single-channel perturbation v is computed and tested with a specific", "type": "text", "cross_page": true } ], "index": 28 }, { "bbox": [ 105, 389, 506, 401 ], "spans": [ { "bbox": [ 105, 389, 210, 401 ], "score": 1.0, "content": "propagation configuration", "type": "text", "cross_page": true }, { "bbox": [ 210, 389, 225, 400 ], "score": 0.89, "content": "\\lambda _ { m }", "type": "inline_equation", "cross_page": true }, { "bbox": [ 225, 389, 243, 401 ], "score": 1.0, "content": "and", "type": "text", "cross_page": true }, { "bbox": [ 243, 389, 254, 400 ], "score": 0.87, "content": "\\lambda _ { d }", "type": "inline_equation", "cross_page": true }, { "bbox": [ 255, 389, 506, 401 ], "score": 1.0, "content": ". Case 3) Consider a use-case where the attacker does not know", "type": "text", "cross_page": true } ], "index": 29 }, { "bbox": [ 105, 399, 506, 412 ], "spans": [ { "bbox": [ 105, 399, 380, 412 ], "score": 1.0, "content": "the spatial propagation model and computes the same perturbation", "type": "text", "cross_page": true }, { "bbox": [ 381, 402, 389, 410 ], "score": 0.32, "content": "\\mathbf { v }", "type": "inline_equation", "cross_page": true }, { "bbox": [ 389, 399, 506, 412 ], "score": 1.0, "content": "for all channels. 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n[10-3v2]l2-norm [mV]γ[%]
ε[mV](a)(b)(c)(a)(b)(c)(a)(b)(c)
13.311.983.4220.815.221.299.8999.9399.89
516.67.7617.399.161.210297.9999.2297.87
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The smaller", "type": "text" } ], "index": 19 }, { "bbox": [ 308, 204, 506, 215 ], "spans": [ { "bbox": [ 308, 204, 385, 215 ], "score": 1.0, "content": "the cross correlation", "type": "text" }, { "bbox": [ 386, 206, 392, 214 ], "score": 0.75, "content": "\\eta", "type": "inline_equation" }, { "bbox": [ 393, 204, 492, 215 ], "score": 1.0, "content": "and the Euclidian distance", "type": "text" }, { "bbox": [ 493, 204, 502, 214 ], "score": 0.81, "content": "\\ell _ { 2 }", "type": "inline_equation" }, { "bbox": [ 502, 204, 506, 215 ], "score": 1.0, "content": "-", "type": "text" } ], "index": 20 }, { "bbox": [ 308, 214, 505, 226 ], "spans": [ { "bbox": [ 308, 214, 461, 226 ], "score": 1.0, "content": "norm, and the higher the cosine similarity", "type": "text" }, { "bbox": [ 461, 216, 468, 225 ], "score": 0.78, "content": "\\gamma", "type": "inline_equation" }, { "bbox": [ 468, 214, 505, 226 ], "score": 1.0, "content": ", the more", "type": "text" } ], "index": 21 }, { "bbox": [ 307, 224, 412, 234 ], "spans": [ { "bbox": [ 307, 224, 412, 234 ], "score": 1.0, "content": "natural the generated attack.", "type": "text" } ], "index": 22 } ], "index": 19.5 } ], "index": 16.75 }, { "type": "image", "bbox": [ 109, 247, 504, 325 ], "blocks": [ { "type": "image_body", "bbox": [ 109, 247, 504, 325 ], "group_id": 1, "lines": [ { "bbox": [ 109, 247, 504, 325 ], "spans": [ { "bbox": [ 109, 247, 504, 325 ], "score": 0.964, "type": "image", "image_path": "0be065a5743b1cee7251d7d88da8df12f3001691d68088f221c3d7a7df59ba26.jpg" } ] } ], "index": 24, "virtual_lines": [ { "bbox": [ 109, 247, 504, 273.0 ], "spans": [], "index": 23 }, { "bbox": [ 109, 273.0, 504, 299.0 ], "spans": [], "index": 24 }, { "bbox": [ 109, 299.0, 504, 325.0 ], "spans": [], "index": 25 } ] }, { "type": "image_caption", "bbox": [ 106, 335, 505, 357 ], "group_id": 1, "lines": [ { "bbox": [ 106, 335, 505, 347 ], "spans": [ { "bbox": [ 106, 335, 404, 347 ], "score": 1.0, "content": "Figure 2: A successful attack (a) without and (b) with derivative loss term (PGD,", "type": "text" }, { "bbox": [ 404, 336, 437, 345 ], "score": 0.86, "content": "\\scriptstyle \\epsilon = 1 0 \\mathrm { m V }", "type": "inline_equation" }, { "bbox": [ 438, 335, 505, 347 ], "score": 1.0, "content": "). The background", "type": "text" } ], "index": 26 }, { "bbox": [ 105, 345, 329, 357 ], "spans": [ { "bbox": [ 105, 345, 329, 357 ], "score": 1.0, "content": "traces show the original signal before the preprocessing filter.", "type": "text" } ], "index": 27 } ], "index": 26.5 } ], "index": 25.25 }, { "type": "text", "bbox": [ 107, 377, 505, 422 ], "lines": [ { "bbox": [ 105, 377, 505, 390 ], "spans": [ { "bbox": [ 105, 377, 505, 390 ], "score": 1.0, "content": "the propagation model: a single-channel perturbation v is computed and tested with a specific", "type": "text" } ], "index": 28 }, { "bbox": [ 105, 389, 506, 401 ], "spans": [ { "bbox": [ 105, 389, 210, 401 ], "score": 1.0, "content": "propagation configuration", "type": "text" }, { "bbox": [ 210, 389, 225, 400 ], "score": 0.89, "content": "\\lambda _ { m }", "type": "inline_equation" }, { "bbox": [ 225, 389, 243, 401 ], "score": 1.0, "content": "and", "type": "text" }, { "bbox": [ 243, 389, 254, 400 ], "score": 0.87, "content": "\\lambda _ { d }", "type": "inline_equation" }, { "bbox": [ 255, 389, 506, 401 ], "score": 1.0, "content": ". Case 3) Consider a use-case where the attacker does not know", "type": "text" } ], "index": 29 }, { "bbox": [ 105, 399, 506, 412 ], "spans": [ { "bbox": [ 105, 399, 380, 412 ], "score": 1.0, "content": "the spatial propagation model and computes the same perturbation", "type": "text" }, { "bbox": [ 381, 402, 389, 410 ], "score": 0.32, "content": "\\mathbf { v }", "type": "inline_equation" }, { "bbox": [ 389, 399, 506, 412 ], "score": 1.0, "content": "for all channels. The actual", "type": "text" } ], "index": 30 }, { "bbox": [ 105, 411, 457, 423 ], "spans": [ { "bbox": [ 105, 411, 457, 423 ], "score": 1.0, "content": "propagation model is applied during testing to model the real-world signal propagation.", "type": "text" } ], "index": 31 } ], "index": 29.5 }, { "type": "text", "bbox": [ 107, 434, 505, 489 ], "lines": [ { "bbox": [ 105, 434, 505, 447 ], "spans": [ { "bbox": [ 105, 434, 505, 447 ], "score": 1.0, "content": "End-to-end algorithm. We illustrate the algorithmic procedure for designing a physiologically", "type": "text" } ], "index": 32 }, { "bbox": [ 105, 445, 505, 457 ], "spans": [ { "bbox": [ 105, 445, 505, 457 ], "score": 1.0, "content": "plausible UAP in Algorithm 1. Analogously, the proposed methods of derivative loss term and model", "type": "text" } ], "index": 33 }, { "bbox": [ 105, 456, 506, 469 ], "spans": [ { "bbox": [ 105, 456, 332, 469 ], "score": 1.0, "content": "propagation are applied with PGD. The hyperparameters", "type": "text" }, { "bbox": [ 332, 458, 340, 466 ], "score": 0.58, "content": "\\alpha", "type": "inline_equation" }, { "bbox": [ 340, 456, 343, 469 ], "score": 1.0, "content": ",", "type": "text" }, { "bbox": [ 344, 457, 351, 468 ], "score": 0.72, "content": "\\beta", "type": "inline_equation" }, { "bbox": [ 352, 456, 506, 469 ], "score": 1.0, "content": ", the number of PGD iterations and the", "type": "text" } ], "index": 34 }, { "bbox": [ 105, 467, 506, 479 ], "spans": [ { "bbox": [ 105, 467, 506, 479 ], "score": 1.0, "content": "restarts, the batch size and the number of epochs in UAP are determined based on a cross-validated", "type": "text" } ], "index": 35 }, { "bbox": [ 105, 478, 229, 491 ], "spans": [ { "bbox": [ 105, 478, 229, 491 ], "score": 1.0, "content": "grid search on the training set.", "type": "text" } ], "index": 36 } ], "index": 34 }, { "type": "title", "bbox": [ 108, 506, 272, 518 ], "lines": [ { "bbox": [ 105, 505, 275, 520 ], "spans": [ { "bbox": [ 105, 505, 275, 520 ], "score": 1.0, "content": "4 EXPERIMENTS AND RESULTS", "type": "text" } ], "index": 37 } ], "index": 37 }, { "type": "text", "bbox": [ 107, 530, 505, 564 ], "lines": [ { "bbox": [ 105, 530, 506, 543 ], "spans": [ { "bbox": [ 105, 530, 506, 543 ], "score": 1.0, "content": "We evaluate our methods on the Physionet EEG Motor Movement/Imagery Dataset (Goldberger et al.,", "type": "text" } ], "index": 38 }, { "bbox": [ 105, 541, 505, 555 ], "spans": [ { "bbox": [ 105, 541, 505, 555 ], "score": 1.0, "content": "2000; Sch) tackling inter-subject challenges, and generalize to subject-specific inter-session dataset", "type": "text" } ], "index": 39 }, { "bbox": [ 105, 552, 379, 566 ], "spans": [ { "bbox": [ 105, 552, 379, 566 ], "score": 1.0, "content": "IV-2a of BCI Competition (Brunner et al., 2008) (See Appendix C).", "type": "text" } ], "index": 40 } ], "index": 39 }, { "type": "text", "bbox": [ 106, 576, 505, 653 ], "lines": [ { "bbox": [ 106, 577, 506, 589 ], "spans": [ { "bbox": [ 106, 577, 506, 589 ], "score": 1.0, "content": "Dataset. The Physionet dataset contains valid EEG recordings of 105 subjects (Dose et al., 2018)", "type": "text" } ], "index": 41 }, { "bbox": [ 106, 587, 505, 599 ], "spans": [ { "bbox": [ 106, 587, 505, 599 ], "score": 1.0, "content": "and is publicly available under Open Data Commons Attribution License v1.0. We use the MI", "type": "text" } ], "index": 42 }, { "bbox": [ 105, 598, 506, 610 ], "spans": [ { "bbox": [ 105, 598, 405, 610 ], "score": 1.0, "content": "recordings that contain tasks of the imagination of left against right fist for", "type": "text" }, { "bbox": [ 405, 599, 417, 609 ], "score": 0.49, "content": "3 \\mathrm { s }", "type": "inline_equation" }, { "bbox": [ 417, 598, 506, 610 ], "score": 1.0, "content": ". The EEG trials were", "type": "text" } ], "index": 43 }, { "bbox": [ 105, 609, 506, 622 ], "spans": [ { "bbox": [ 105, 609, 165, 622 ], "score": 1.0, "content": "recorded with", "type": "text" }, { "bbox": [ 165, 609, 199, 620 ], "score": 0.91, "content": "N _ { c h } { = } 6 4", "type": "inline_equation" }, { "bbox": [ 200, 609, 284, 622 ], "score": 1.0, "content": "channels sampled at", "type": "text" }, { "bbox": [ 285, 609, 331, 620 ], "score": 0.91, "content": "F _ { s } { = } 1 6 0 \\mathrm { H z }", "type": "inline_equation" }, { "bbox": [ 331, 609, 370, 622 ], "score": 1.0, "content": ", yielding", "type": "text" }, { "bbox": [ 370, 609, 434, 620 ], "score": 0.89, "content": "N _ { s } { = } 3 { \\cdot } 1 6 0 { = } 4 8 0", "type": "inline_equation" }, { "bbox": [ 434, 609, 506, 622 ], "score": 1.0, "content": "samples per trial.", "type": "text" } ], "index": 44 }, { "bbox": [ 105, 620, 505, 633 ], "spans": [ { "bbox": [ 105, 620, 505, 633 ], "score": 1.0, "content": "Additional baseline runs provide resting-state data, where the subjects did not perform any tasks", "type": "text" } ], "index": 45 }, { "bbox": [ 104, 631, 507, 644 ], "spans": [ { "bbox": [ 104, 631, 380, 644 ], "score": 1.0, "content": "while having eyes open. Overall, we get a total of 6615 trials with", "type": "text" }, { "bbox": [ 381, 631, 407, 642 ], "score": 0.91, "content": "N _ { c l } { = } 3", "type": "inline_equation" }, { "bbox": [ 408, 631, 507, 644 ], "score": 1.0, "content": "balanced classes “left”,", "type": "text" } ], "index": 46 }, { "bbox": [ 105, 642, 182, 654 ], "spans": [ { "bbox": [ 105, 642, 182, 654 ], "score": 1.0, "content": "“right”, and “rest.”", "type": "text" } ], "index": 47 } ], "index": 44 }, { "type": "text", "bbox": [ 107, 665, 505, 732 ], "lines": [ { "bbox": [ 106, 665, 505, 678 ], "spans": [ { "bbox": [ 106, 665, 505, 678 ], "score": 1.0, "content": "Training and validation. We train and validate both the classification models and the generated", "type": "text" } ], "index": 48 }, { "bbox": [ 106, 676, 506, 689 ], "spans": [ { "bbox": [ 106, 676, 506, 689 ], "score": 1.0, "content": "adversarial examples with a 5-fold cross-validation, splitting the dataset into 84 subjects used for", "type": "text" } ], "index": 49 }, { "bbox": [ 105, 687, 506, 700 ], "spans": [ { "bbox": [ 105, 687, 506, 700 ], "score": 1.0, "content": "training and 21 subjects used for validation to effectively test the model on inter-subject variability.", "type": "text" } ], "index": 50 }, { "bbox": [ 106, 698, 506, 711 ], "spans": [ { "bbox": [ 106, 698, 506, 711 ], "score": 1.0, "content": "Similar to Wang et al. (2020), which achieved SoA performance on this dataset, the baseline model is", "type": "text" } ], "index": 51 }, { "bbox": [ 105, 709, 506, 722 ], "spans": [ { "bbox": [ 105, 709, 267, 722 ], "score": 1.0, "content": "trained for 100 epochs using Adam with", "type": "text" }, { "bbox": [ 267, 710, 296, 721 ], "score": 0.86, "content": "\\beta _ { 1 } { = } 0 . 9", "type": "inline_equation" }, { "bbox": [ 297, 709, 300, 722 ], "score": 1.0, "content": ",", "type": "text" }, { "bbox": [ 300, 710, 339, 721 ], "score": 0.87, "content": "\\beta _ { 2 } { = } 0 . 9 9 9", "type": "inline_equation" }, { "bbox": [ 339, 709, 506, 722 ], "score": 1.0, "content": ", and batch size of 16. 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n[10-3v2]l2-norm [mV]γ[%]
ε[mV](a)(b)(c)(a)(b)(c)(a)(b)(c)
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The smaller", "type": "text" } ], "index": 19 }, { "bbox": [ 308, 204, 506, 215 ], "spans": [ { "bbox": [ 308, 204, 385, 215 ], "score": 1.0, "content": "the cross correlation", "type": "text" }, { "bbox": [ 386, 206, 392, 214 ], "score": 0.75, "content": "\\eta", "type": "inline_equation" }, { "bbox": [ 393, 204, 492, 215 ], "score": 1.0, "content": "and the Euclidian distance", "type": "text" }, { "bbox": [ 493, 204, 502, 214 ], "score": 0.81, "content": "\\ell _ { 2 }", "type": "inline_equation" }, { "bbox": [ 502, 204, 506, 215 ], "score": 1.0, "content": "-", "type": "text" } ], "index": 20 }, { "bbox": [ 308, 214, 505, 226 ], "spans": [ { "bbox": [ 308, 214, 461, 226 ], "score": 1.0, "content": "norm, and the higher the cosine similarity", "type": "text" }, { "bbox": [ 461, 216, 468, 225 ], "score": 0.78, "content": "\\gamma", "type": "inline_equation" }, { "bbox": [ 468, 214, 505, 226 ], "score": 1.0, "content": ", the more", "type": "text" } ], "index": 21 }, { "bbox": [ 307, 224, 412, 234 ], "spans": [ { "bbox": [ 307, 224, 412, 234 ], "score": 1.0, "content": "natural the generated attack.", "type": "text" } ], "index": 22 } ], "index": 19.5 } ], "index": 16.75 }, { "type": "image", "bbox": [ 109, 247, 504, 325 ], "blocks": [ { "type": "image_body", "bbox": [ 109, 247, 504, 325 ], "group_id": 1, "lines": [ { "bbox": [ 109, 247, 504, 325 ], "spans": [ { "bbox": [ 109, 247, 504, 325 ], "score": 0.964, "type": "image", "image_path": "0be065a5743b1cee7251d7d88da8df12f3001691d68088f221c3d7a7df59ba26.jpg" } ] } ], "index": 24, "virtual_lines": [ { "bbox": [ 109, 247, 504, 273.0 ], "spans": [], "index": 23 }, { "bbox": [ 109, 273.0, 504, 299.0 ], "spans": [], "index": 24 }, { "bbox": [ 109, 299.0, 504, 325.0 ], "spans": [], "index": 25 } ] }, { "type": "image_caption", "bbox": [ 106, 335, 505, 357 ], "group_id": 1, "lines": [ { "bbox": [ 106, 335, 505, 347 ], "spans": [ { "bbox": [ 106, 335, 404, 347 ], "score": 1.0, "content": "Figure 2: A successful attack (a) without and (b) with derivative loss term (PGD,", "type": "text" }, { "bbox": [ 404, 336, 437, 345 ], "score": 0.86, "content": "\\scriptstyle \\epsilon = 1 0 \\mathrm { m V }", "type": "inline_equation" }, { "bbox": [ 438, 335, 505, 347 ], "score": 1.0, "content": "). The background", "type": "text" } ], "index": 26 }, { "bbox": [ 105, 345, 329, 357 ], "spans": [ { "bbox": [ 105, 345, 329, 357 ], "score": 1.0, "content": "traces show the original signal before the preprocessing filter.", "type": "text" } ], "index": 27 } ], "index": 26.5 } ], "index": 25.25 }, { "type": "text", "bbox": [ 107, 377, 505, 422 ], "lines": [], "index": 29.5, "bbox_fs": [ 105, 377, 506, 423 ], "lines_deleted": true }, { "type": "text", "bbox": [ 107, 434, 505, 489 ], "lines": [ { "bbox": [ 105, 434, 505, 447 ], "spans": [ { "bbox": [ 105, 434, 505, 447 ], "score": 1.0, "content": "End-to-end algorithm. We illustrate the algorithmic procedure for designing a physiologically", "type": "text" } ], "index": 32 }, { "bbox": [ 105, 445, 505, 457 ], "spans": [ { "bbox": [ 105, 445, 505, 457 ], "score": 1.0, "content": "plausible UAP in Algorithm 1. Analogously, the proposed methods of derivative loss term and model", "type": "text" } ], "index": 33 }, { "bbox": [ 105, 456, 506, 469 ], "spans": [ { "bbox": [ 105, 456, 332, 469 ], "score": 1.0, "content": "propagation are applied with PGD. 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The Physionet dataset contains valid EEG recordings of 105 subjects (Dose et al., 2018)", "type": "text" } ], "index": 41 }, { "bbox": [ 106, 587, 505, 599 ], "spans": [ { "bbox": [ 106, 587, 505, 599 ], "score": 1.0, "content": "and is publicly available under Open Data Commons Attribution License v1.0. We use the MI", "type": "text" } ], "index": 42 }, { "bbox": [ 105, 598, 506, 610 ], "spans": [ { "bbox": [ 105, 598, 405, 610 ], "score": 1.0, "content": "recordings that contain tasks of the imagination of left against right fist for", "type": "text" }, { "bbox": [ 405, 599, 417, 609 ], "score": 0.49, "content": "3 \\mathrm { s }", "type": "inline_equation" }, { "bbox": [ 417, 598, 506, 610 ], "score": 1.0, "content": ". The EEG trials were", "type": "text" } ], "index": 43 }, { "bbox": [ 105, 609, 506, 622 ], "spans": [ { "bbox": [ 105, 609, 165, 622 ], "score": 1.0, "content": "recorded with", "type": "text" }, { "bbox": [ 165, 609, 199, 620 ], "score": 0.91, "content": "N _ { c h } { = } 6 4", "type": "inline_equation" }, { "bbox": [ 200, 609, 284, 622 ], "score": 1.0, "content": "channels sampled at", "type": "text" }, { "bbox": [ 285, 609, 331, 620 ], "score": 0.91, "content": "F _ { s } { = } 1 6 0 \\mathrm { H z }", "type": "inline_equation" }, { "bbox": [ 331, 609, 370, 622 ], "score": 1.0, "content": ", yielding", "type": "text" }, { "bbox": [ 370, 609, 434, 620 ], "score": 0.89, "content": "N _ { s } { = } 3 { \\cdot } 1 6 0 { = } 4 8 0", "type": "inline_equation" }, { "bbox": [ 434, 609, 506, 622 ], "score": 1.0, "content": "samples per trial.", "type": "text" } ], "index": 44 }, { "bbox": [ 105, 620, 505, 633 ], "spans": [ { "bbox": [ 105, 620, 505, 633 ], "score": 1.0, "content": "Additional baseline runs provide resting-state data, where the subjects did not perform any tasks", "type": "text" } ], "index": 45 }, { "bbox": [ 104, 631, 507, 644 ], "spans": [ { "bbox": [ 104, 631, 380, 644 ], "score": 1.0, "content": "while having eyes open. Overall, we get a total of 6615 trials with", "type": "text" }, { "bbox": [ 381, 631, 407, 642 ], "score": 0.91, "content": "N _ { c l } { = } 3", "type": "inline_equation" }, { "bbox": [ 408, 631, 507, 644 ], "score": 1.0, "content": "balanced classes “left”,", "type": "text" } ], "index": 46 }, { "bbox": [ 105, 642, 182, 654 ], "spans": [ { "bbox": [ 105, 642, 182, 654 ], "score": 1.0, "content": "“right”, and “rest.”", "type": "text" } ], "index": 47 } ], "index": 44, "bbox_fs": [ 104, 577, 507, 654 ] }, { "type": "text", "bbox": [ 107, 665, 505, 732 ], "lines": [ { "bbox": [ 106, 665, 505, 678 ], "spans": [ { "bbox": [ 106, 665, 505, 678 ], "score": 1.0, "content": "Training and validation. We train and validate both the classification models and the generated", "type": "text" } ], "index": 48 }, { "bbox": [ 106, 676, 506, 689 ], "spans": [ { "bbox": [ 106, 676, 506, 689 ], "score": 1.0, "content": "adversarial examples with a 5-fold cross-validation, splitting the dataset into 84 subjects used for", "type": "text" } ], "index": 49 }, { "bbox": [ 105, 687, 506, 700 ], "spans": [ { "bbox": [ 105, 687, 506, 700 ], "score": 1.0, "content": "training and 21 subjects used for validation to effectively test the model on inter-subject variability.", "type": "text" } ], "index": 50 }, { "bbox": [ 106, 698, 506, 711 ], "spans": [ { "bbox": [ 106, 698, 506, 711 ], "score": 1.0, "content": "Similar to Wang et al. 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We first analyze the instance-based attacks without considering", "type": "text" } ], "index": 34 }, { "bbox": [ 106, 622, 505, 634 ], "spans": [ { "bbox": [ 106, 622, 505, 634 ], "score": 1.0, "content": "the propagation model (Case 1), depicted in Figure 1. We compare our methods against random", "type": "text" } ], "index": 35 }, { "bbox": [ 105, 632, 506, 646 ], "spans": [ { "bbox": [ 105, 632, 191, 646 ], "score": 1.0, "content": "noise with amplitude", "type": "text" }, { "bbox": [ 191, 635, 198, 643 ], "score": 0.43, "content": "\\epsilon", "type": "inline_equation" }, { "bbox": [ 198, 632, 506, 646 ], "score": 1.0, "content": "as in (Zhang & Wu, 2019), FGSM that is the same as in (Zhang & Wu, 2019)", "type": "text" } ], "index": 36 }, { "bbox": [ 106, 644, 505, 657 ], "spans": [ { "bbox": [ 106, 644, 505, 657 ], "score": 1.0, "content": "with targeted scenario, and a UAP designed specifically for EEG (Liu et al., 2021). For both FGSM", "type": "text" } ], "index": 37 }, { "bbox": [ 105, 654, 506, 668 ], "spans": [ { "bbox": [ 105, 654, 390, 668 ], "score": 1.0, "content": "and PGD, the ASR increases together with the maximum amplitude", "type": "text" }, { "bbox": [ 390, 657, 396, 665 ], "score": 0.65, "content": "\\epsilon", "type": "inline_equation" }, { "bbox": [ 397, 654, 506, 668 ], "score": 1.0, "content": "of the perturbation. They", "type": "text" } ], "index": 38 }, { "bbox": [ 106, 665, 505, 678 ], "spans": [ { "bbox": [ 106, 665, 505, 678 ], "score": 1.0, "content": "always outperform the random noise, with PGD performing slightly better than FGSM. 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Figure 2a", "type": "text" } ], "index": 41 }, { "bbox": [ 105, 698, 507, 712 ], "spans": [ { "bbox": [ 105, 698, 507, 712 ], "score": 1.0, "content": "shows the signals of a successful attack using PGD. The adversarial perturbation has a square-", "type": "text" } ], "index": 42 }, { "bbox": [ 106, 709, 505, 722 ], "spans": [ { "bbox": [ 106, 709, 505, 722 ], "score": 1.0, "content": "wave form which negatively affects the natural shape of the EEG signal. 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} 4 0 \\mathrm { H z }", "type": "inline_equation" }, { "bbox": [ 384, 551, 506, 564 ], "score": 1.0, "content": "(Lawhern et al., 2018) is used", "type": "text" } ], "index": 30 }, { "bbox": [ 105, 563, 505, 574 ], "spans": [ { "bbox": [ 105, 563, 505, 574 ], "score": 1.0, "content": "as preprocessing step in both baseline and attack experiments.To determine the ASR, we compute the", "type": "text" } ], "index": 31 }, { "bbox": [ 106, 574, 505, 585 ], "spans": [ { "bbox": [ 106, 574, 505, 585 ], "score": 1.0, "content": "ratio between the successfully fooled trials, i.e., trials now classified as “rest”, and the total number", "type": "text" } ], "index": 32 }, { "bbox": [ 105, 584, 489, 596 ], "spans": [ { "bbox": [ 105, 584, 489, 596 ], "score": 1.0, "content": "of attacked trials, where we only consider the ones initially correctly classified as “left”/“right”.", "type": "text" } ], "index": 33 } ], "index": 31.5, "bbox_fs": [ 105, 551, 506, 596 ] }, { "type": "text", "bbox": [ 107, 610, 505, 732 ], "lines": [ { "bbox": [ 105, 609, 506, 624 ], "spans": [ { "bbox": [ 105, 609, 506, 624 ], "score": 1.0, "content": "Physiologically plausible attacks. We first analyze the instance-based attacks without considering", "type": "text" } ], "index": 34 }, { "bbox": [ 106, 622, 505, 634 ], "spans": [ { "bbox": [ 106, 622, 505, 634 ], "score": 1.0, "content": "the propagation model (Case 1), depicted in Figure 1. We compare our methods against random", "type": "text" } ], "index": 35 }, { "bbox": [ 105, 632, 506, 646 ], "spans": [ { "bbox": [ 105, 632, 191, 646 ], "score": 1.0, "content": "noise with amplitude", "type": "text" }, { "bbox": [ 191, 635, 198, 643 ], "score": 0.43, "content": "\\epsilon", "type": "inline_equation" }, { "bbox": [ 198, 632, 506, 646 ], "score": 1.0, "content": "as in (Zhang & Wu, 2019), FGSM that is the same as in (Zhang & Wu, 2019)", "type": "text" } ], "index": 36 }, { "bbox": [ 106, 644, 505, 657 ], "spans": [ { "bbox": [ 106, 644, 505, 657 ], "score": 1.0, "content": "with targeted scenario, and a UAP designed specifically for EEG (Liu et al., 2021). For both FGSM", "type": "text" } ], "index": 37 }, { "bbox": [ 105, 654, 506, 668 ], "spans": [ { "bbox": [ 105, 654, 390, 668 ], "score": 1.0, "content": "and PGD, the ASR increases together with the maximum amplitude", "type": "text" }, { "bbox": [ 390, 657, 396, 665 ], "score": 0.65, "content": "\\epsilon", "type": "inline_equation" }, { "bbox": [ 397, 654, 506, 668 ], "score": 1.0, "content": "of the perturbation. They", "type": "text" } ], "index": 38 }, { "bbox": [ 106, 665, 505, 678 ], "spans": [ { "bbox": [ 106, 665, 505, 678 ], "score": 1.0, "content": "always outperform the random noise, with PGD performing slightly better than FGSM. They reach", "type": "text" } ], "index": 39 }, { "bbox": [ 106, 677, 505, 689 ], "spans": [ { "bbox": [ 106, 677, 194, 689 ], "score": 1.0, "content": "the maximum ASR of", "type": "text" }, { "bbox": [ 195, 677, 226, 687 ], "score": 0.88, "content": "9 9 . 9 7 \\%", "type": "inline_equation" }, { "bbox": [ 226, 677, 247, 689 ], "score": 1.0, "content": "with", "type": "text" }, { "bbox": [ 247, 677, 273, 687 ], "score": 0.59, "content": "1 0 \\mathrm { m V } .", "type": "inline_equation" }, { "bbox": [ 273, 677, 473, 689 ], "score": 1.0, "content": "The post-attack classification accuracy drops from", "type": "text" }, { "bbox": [ 473, 677, 505, 687 ], "score": 0.86, "content": "7 4 . 7 8 \\%", "type": "inline_equation" } ], "index": 40 }, { "bbox": [ 105, 687, 505, 700 ], "spans": [ { "bbox": [ 105, 687, 116, 700 ], "score": 1.0, "content": "to", "type": "text" }, { "bbox": [ 117, 688, 136, 698 ], "score": 0.86, "content": "48 \\%", "type": "inline_equation" }, { "bbox": [ 136, 687, 259, 700 ], "score": 1.0, "content": "for a perturbation amplitude of", "type": "text" }, { "bbox": [ 260, 688, 283, 698 ], "score": 0.64, "content": "2 \\mathrm { m V }", "type": "inline_equation" }, { "bbox": [ 283, 687, 310, 700 ], "score": 1.0, "content": "and to", "type": "text" }, { "bbox": [ 310, 688, 329, 698 ], "score": 0.87, "content": "33 \\%", "type": "inline_equation" }, { "bbox": [ 330, 687, 344, 700 ], "score": 1.0, "content": "for", "type": "text" }, { "bbox": [ 345, 688, 372, 698 ], "score": 0.75, "content": "1 0 \\mathrm { m V }", "type": "inline_equation" }, { "bbox": [ 372, 687, 505, 700 ], "score": 1.0, "content": "and higher amplitudes. Figure 2a", "type": "text" } ], "index": 41 }, { "bbox": [ 105, 698, 507, 712 ], "spans": [ { "bbox": [ 105, 698, 507, 712 ], "score": 1.0, "content": "shows the signals of a successful attack using PGD. The adversarial perturbation has a square-", "type": "text" } ], "index": 42 }, { "bbox": [ 106, 709, 505, 722 ], "spans": [ { "bbox": [ 106, 709, 505, 722 ], "score": 1.0, "content": "wave form which negatively affects the natural shape of the EEG signal. By adding the proposed", "type": "text" } ], "index": 43 }, { "bbox": [ 106, 721, 505, 733 ], "spans": [ { "bbox": [ 106, 721, 505, 733 ], "score": 1.0, "content": "derivative term, the square-wave artifacts are significantly reduced (2b), making the perturbation more", "type": "text" } ], "index": 44 }, { "bbox": [ 106, 83, 505, 95 ], "spans": [ { "bbox": [ 106, 83, 505, 95 ], "score": 1.0, "content": "physiologically plausible. When comparing the power spectral density of the original and attacked", "type": "text", "cross_page": true } ], "index": 0 }, { "bbox": [ 105, 93, 506, 107 ], "spans": [ { "bbox": [ 105, 93, 506, 107 ], "score": 1.0, "content": "signals, the attacked signal designed without derivative presents large components in low frequencies,", "type": "text", "cross_page": true } ], "index": 1 }, { "bbox": [ 105, 104, 506, 118 ], "spans": [ { "bbox": [ 105, 104, 506, 118 ], "score": 1.0, "content": "making it more easily detectable. Whereas the attack with derivative loss better resembles the power", "type": "text", "cross_page": true } ], "index": 2 }, { "bbox": [ 105, 115, 506, 128 ], "spans": [ { "bbox": [ 105, 115, 506, 128 ], "score": 1.0, "content": "spectral density of the original signal (see Appendix D). Moreover, the introduction of the derivative", "type": "text", "cross_page": true } ], "index": 3 }, { "bbox": [ 106, 126, 505, 139 ], "spans": [ { "bbox": [ 106, 126, 505, 139 ], "score": 1.0, "content": "term does not degrade the ASR (Figure 1). The quantitative measures between the original and the", "type": "text", "cross_page": true } ], "index": 4 }, { "bbox": [ 105, 137, 506, 150 ], "spans": [ { "bbox": [ 105, 137, 506, 150 ], "score": 1.0, "content": "adversarial samples in Table 1 demonstrate that our proposed method with derivative term generates", "type": "text", "cross_page": true } ], "index": 5 }, { "bbox": [ 106, 149, 505, 161 ], "spans": [ { "bbox": [ 106, 149, 505, 161 ], "score": 1.0, "content": "adversarial samples that are more similar to the original EEG, allowing them to remain imperceptible", "type": "text", "cross_page": true } ], "index": 6 }, { "bbox": [ 105, 159, 506, 172 ], "spans": [ { "bbox": [ 105, 159, 168, 172 ], "score": 1.0, "content": "even with high", "type": "text", "cross_page": true }, { "bbox": [ 168, 161, 174, 169 ], "score": 0.56, "content": "\\epsilon", "type": "inline_equation", "cross_page": true }, { "bbox": [ 174, 159, 506, 172 ], "score": 1.0, "content": "(Appendix D). We reproduce the attacks using a Gaussian kernel as in (Han et al.,", "type": "text", "cross_page": true } ], "index": 7 }, { "bbox": [ 105, 169, 506, 184 ], "spans": [ { "bbox": [ 105, 169, 506, 184 ], "score": 1.0, "content": "2020). After tuning the kernel size and variance of the Gaussian kernel, the method could not improve", "type": "text", "cross_page": true } ], "index": 8 }, { "bbox": [ 106, 181, 506, 194 ], "spans": [ { "bbox": [ 106, 181, 506, 194 ], "score": 1.0, "content": "the plausibility metrics. The inferior performance of the Gaussian kernel could stem from the different", "type": "text", "cross_page": true } ], "index": 9 }, { "bbox": [ 105, 191, 496, 206 ], "spans": [ { "bbox": [ 105, 191, 496, 206 ], "score": 1.0, "content": "nature of the signal: it was originally designed for ECGs which have a pseudo-periodic structure.", "type": "text", "cross_page": true } ], "index": 10 } ], "index": 39, "bbox_fs": [ 105, 609, 507, 733 ] } ] }, { "preproc_blocks": [ { "type": "text", "bbox": [ 107, 82, 505, 203 ], "lines": [ { "bbox": [ 106, 83, 505, 95 ], "spans": [ { "bbox": [ 106, 83, 505, 95 ], "score": 1.0, "content": "physiologically plausible. When comparing the power spectral density of the original and attacked", "type": "text" } ], "index": 0 }, { "bbox": [ 105, 93, 506, 107 ], "spans": [ { "bbox": [ 105, 93, 506, 107 ], "score": 1.0, "content": "signals, the attacked signal designed without derivative presents large components in low frequencies,", "type": "text" } ], "index": 1 }, { "bbox": [ 105, 104, 506, 118 ], "spans": [ { "bbox": [ 105, 104, 506, 118 ], "score": 1.0, "content": "making it more easily detectable. Whereas the attack with derivative loss better resembles the power", "type": "text" } ], "index": 2 }, { "bbox": [ 105, 115, 506, 128 ], "spans": [ { "bbox": [ 105, 115, 506, 128 ], "score": 1.0, "content": "spectral density of the original signal (see Appendix D). Moreover, the introduction of the derivative", "type": "text" } ], "index": 3 }, { "bbox": [ 106, 126, 505, 139 ], "spans": [ { "bbox": [ 106, 126, 505, 139 ], "score": 1.0, "content": "term does not degrade the ASR (Figure 1). The quantitative measures between the original and the", "type": "text" } ], "index": 4 }, { "bbox": [ 105, 137, 506, 150 ], "spans": [ { "bbox": [ 105, 137, 506, 150 ], "score": 1.0, "content": "adversarial samples in Table 1 demonstrate that our proposed method with derivative term generates", "type": "text" } ], "index": 5 }, { "bbox": [ 106, 149, 505, 161 ], "spans": [ { "bbox": [ 106, 149, 505, 161 ], "score": 1.0, "content": "adversarial samples that are more similar to the original EEG, allowing them to remain imperceptible", "type": "text" } ], "index": 6 }, { "bbox": [ 105, 159, 506, 172 ], "spans": [ { "bbox": [ 105, 159, 168, 172 ], "score": 1.0, "content": "even with high", "type": "text" }, { "bbox": [ 168, 161, 174, 169 ], "score": 0.56, "content": "\\epsilon", "type": "inline_equation" }, { "bbox": [ 174, 159, 506, 172 ], "score": 1.0, "content": "(Appendix D). We reproduce the attacks using a Gaussian kernel as in (Han et al.,", "type": "text" } ], "index": 7 }, { "bbox": [ 105, 169, 506, 184 ], "spans": [ { "bbox": [ 105, 169, 506, 184 ], "score": 1.0, "content": "2020). After tuning the kernel size and variance of the Gaussian kernel, the method could not improve", "type": "text" } ], "index": 8 }, { "bbox": [ 106, 181, 506, 194 ], "spans": [ { "bbox": [ 106, 181, 506, 194 ], "score": 1.0, "content": "the plausibility metrics. The inferior performance of the Gaussian kernel could stem from the different", "type": "text" } ], "index": 9 }, { "bbox": [ 105, 191, 496, 206 ], "spans": [ { "bbox": [ 105, 191, 496, 206 ], "score": 1.0, "content": "nature of the signal: it was originally designed for ECGs which have a pseudo-periodic structure.", "type": "text" } ], "index": 10 } ], "index": 5 }, { "type": "text", "bbox": [ 107, 209, 505, 275 ], "lines": [ { "bbox": [ 106, 209, 505, 221 ], "spans": [ { "bbox": [ 106, 209, 505, 221 ], "score": 1.0, "content": "We extend the application of the derivative term to the UAP attack, while still not considering the", "type": "text" } ], "index": 11 }, { "bbox": [ 104, 219, 503, 233 ], "spans": [ { "bbox": [ 104, 219, 497, 233 ], "score": 1.0, "content": "propagation model (Case 1). Figure 1 shows a comparison in performance for different values of", "type": "text" }, { "bbox": [ 498, 222, 503, 230 ], "score": 0.59, "content": "\\epsilon", "type": "inline_equation" } ], "index": 12 }, { "bbox": [ 105, 230, 505, 243 ], "spans": [ { "bbox": [ 105, 230, 290, 243 ], "score": 1.0, "content": "The saturation in ASR is reached with higher", "type": "text" }, { "bbox": [ 290, 233, 296, 241 ], "score": 0.59, "content": "\\epsilon", "type": "inline_equation" }, { "bbox": [ 296, 230, 316, 243 ], "score": 1.0, "content": ", i.e.,", "type": "text" }, { "bbox": [ 317, 231, 349, 241 ], "score": 0.88, "content": "9 9 . 9 4 \\%", "type": "inline_equation" }, { "bbox": [ 349, 230, 370, 243 ], "score": 1.0, "content": "with", "type": "text" }, { "bbox": [ 370, 231, 397, 241 ], "score": 0.64, "content": "5 0 \\mathrm { m V } .", "type": "inline_equation" }, { "bbox": [ 397, 230, 505, 243 ], "score": 1.0, "content": ". This is expected since the", "type": "text" } ], "index": 13 }, { "bbox": [ 105, 241, 506, 254 ], "spans": [ { "bbox": [ 105, 241, 506, 254 ], "score": 1.0, "content": "UAP is a more difficult attack where a single set of perturbations per EEG channel is generated for", "type": "text" } ], "index": 14 }, { "bbox": [ 105, 252, 506, 265 ], "spans": [ { "bbox": [ 105, 252, 506, 265 ], "score": 1.0, "content": "all the test samples. Likewise in PGD, the ASR does not drop with the addition of the derivative term.", "type": "text" } ], "index": 15 }, { "bbox": [ 105, 263, 500, 276 ], "spans": [ { "bbox": [ 105, 263, 500, 276 ], "score": 1.0, "content": "We reproduce the UAP proposed by (Liu et al., 2021). Our UAP consistently reaches higher ASR.", "type": "text" } ], "index": 16 } ], "index": 13.5 }, { "type": "text", "bbox": [ 107, 290, 505, 444 ], "lines": [ { "bbox": [ 106, 290, 505, 303 ], "spans": [ { "bbox": [ 106, 290, 505, 303 ], "score": 1.0, "content": "Spatial Propagation. Finally, we introduce the spatial constraints in the signal propagation over the", "type": "text" } ], "index": 17 }, { "bbox": [ 106, 301, 505, 314 ], "spans": [ { "bbox": [ 106, 301, 505, 314 ], "score": 1.0, "content": "scalp (Case 2). We consider 9 different scenarios by combining 3 realistic attenuation configurations", "type": "text" } ], "index": 18 }, { "bbox": [ 107, 309, 506, 327 ], "spans": [ { "bbox": [ 107, 312, 177, 324 ], "score": 0.91, "content": "\\lambda _ { m } \\ \\in \\ \\{ 1 , 5 , 1 5 \\}", "type": "inline_equation" }, { "bbox": [ 178, 309, 299, 327 ], "score": 1.0, "content": "with 3 delay configurations", "type": "text" }, { "bbox": [ 299, 312, 395, 324 ], "score": 0.93, "content": "\\lambda _ { d } \\ \\in \\ \\{ 0 . 1 , 0 . 3 , 0 . 5 6 3 \\}", "type": "inline_equation" }, { "bbox": [ 396, 309, 506, 327 ], "score": 1.0, "content": ", which capture the range", "type": "text" } ], "index": 19 }, { "bbox": [ 105, 322, 505, 336 ], "spans": [ { "bbox": [ 105, 322, 505, 336 ], "score": 1.0, "content": "described in Section 3.2. For evaluating the highest achievable attack efficiency, we test a scenario", "type": "text" } ], "index": 20 }, { "bbox": [ 106, 334, 506, 347 ], "spans": [ { "bbox": [ 106, 334, 506, 347 ], "score": 1.0, "content": "where the attacker is assumed to know the propagation model: the adversarial perturbation is generated", "type": "text" } ], "index": 21 }, { "bbox": [ 105, 344, 505, 358 ], "spans": [ { "bbox": [ 105, 344, 272, 358 ], "score": 1.0, "content": "and evaluated on fixed spatial parameters", "type": "text" }, { "bbox": [ 272, 345, 287, 356 ], "score": 0.9, "content": "\\lambda _ { m }", "type": "inline_equation" }, { "bbox": [ 287, 344, 304, 358 ], "score": 1.0, "content": "and", "type": "text" }, { "bbox": [ 305, 345, 317, 356 ], "score": 0.88, "content": "\\lambda _ { d }", "type": "inline_equation" }, { "bbox": [ 317, 344, 505, 358 ], "score": 1.0, "content": ", shown in Figure 4, where the ASR reaches up", "type": "text" } ], "index": 22 }, { "bbox": [ 106, 355, 506, 368 ], "spans": [ { "bbox": [ 106, 355, 116, 368 ], "score": 1.0, "content": "to", "type": "text" }, { "bbox": [ 117, 356, 143, 366 ], "score": 0.87, "content": "6 9 . 2 \\%", "type": "inline_equation" }, { "bbox": [ 144, 355, 202, 368 ], "score": 1.0, "content": "with PGD and", "type": "text" }, { "bbox": [ 202, 356, 228, 366 ], "score": 0.86, "content": "4 5 . 6 \\%", "type": "inline_equation" }, { "bbox": [ 229, 355, 280, 368 ], "score": 1.0, "content": "with UAP at", "type": "text" }, { "bbox": [ 280, 356, 306, 366 ], "score": 0.61, "content": "5 0 \\mathrm { m V } .", "type": "inline_equation" }, { "bbox": [ 306, 355, 506, 368 ], "score": 1.0, "content": "Figure 3 depicts an example of a successful attack", "type": "text" } ], "index": 23 }, { "bbox": [ 106, 367, 506, 380 ], "spans": [ { "bbox": [ 106, 367, 506, 380 ], "score": 1.0, "content": "with the highest perturbation amplitude. The introduction of the spatial constraints makes the attack", "type": "text" } ], "index": 24 }, { "bbox": [ 105, 377, 506, 390 ], "spans": [ { "bbox": [ 105, 377, 506, 390 ], "score": 1.0, "content": "problem harder yielding seldom square distortions. However, the resulting EEG signals still resemble", "type": "text" } ], "index": 25 }, { "bbox": [ 105, 388, 506, 403 ], "spans": [ { "bbox": [ 105, 388, 506, 403 ], "score": 1.0, "content": "physiological random processes typical of EEGs. Next, we ablate the spatial constraints during", "type": "text" } ], "index": 26 }, { "bbox": [ 105, 399, 506, 413 ], "spans": [ { "bbox": [ 105, 399, 506, 413 ], "score": 1.0, "content": "generation and test the resulting perturbations on the 9 above-mentioned scenarios (Case 3). The", "type": "text" } ], "index": 27 }, { "bbox": [ 105, 410, 505, 423 ], "spans": [ { "bbox": [ 105, 410, 263, 423 ], "score": 1.0, "content": "ASR drops significantly, especially for", "type": "text" }, { "bbox": [ 264, 411, 289, 422 ], "score": 0.91, "content": "\\lambda _ { m } { = } 5", "type": "inline_equation" }, { "bbox": [ 290, 410, 307, 423 ], "score": 1.0, "content": "and", "type": "text" }, { "bbox": [ 307, 411, 338, 422 ], "score": 0.91, "content": "\\lambda _ { m } { = } 1 5", "type": "inline_equation" }, { "bbox": [ 338, 410, 505, 423 ], "score": 1.0, "content": "where the attenuation of the perturbation", "type": "text" } ], "index": 28 }, { "bbox": [ 105, 421, 506, 434 ], "spans": [ { "bbox": [ 105, 421, 506, 434 ], "score": 1.0, "content": "over the scalp is greater (see Figure 7), and with the global UAP attack, where the attacker does not", "type": "text" } ], "index": 29 }, { "bbox": [ 106, 433, 270, 445 ], "spans": [ { "bbox": [ 106, 433, 270, 445 ], "score": 1.0, "content": "have access to the attacked EEG signals.", "type": "text" } ], "index": 30 } ], "index": 23.5 }, { "type": "text", "bbox": [ 107, 450, 505, 538 ], "lines": [ { "bbox": [ 106, 450, 506, 462 ], "spans": [ { "bbox": [ 106, 450, 506, 462 ], "score": 1.0, "content": "Our spatial propagation models allows us to identify the vulnerability of the individual EEG channels.", "type": "text" } ], "index": 31 }, { "bbox": [ 105, 460, 505, 473 ], "spans": [ { "bbox": [ 105, 460, 505, 473 ], "score": 1.0, "content": "Figure 5 shows the ASR when initiating an attack from a specific channel (T9, T10, etc.) and", "type": "text" } ], "index": 32 }, { "bbox": [ 105, 471, 506, 484 ], "spans": [ { "bbox": [ 105, 471, 421, 484 ], "score": 1.0, "content": "propagating it to the rest of the head. In the case with the greatest attenuation", "type": "text" }, { "bbox": [ 421, 472, 455, 483 ], "score": 0.86, "content": "\\left( \\lambda _ { m } = 1 5 \\right)", "type": "inline_equation" }, { "bbox": [ 456, 471, 506, 484 ], "score": 1.0, "content": "we find the", "type": "text" } ], "index": 33 }, { "bbox": [ 105, 482, 505, 495 ], "spans": [ { "bbox": [ 105, 482, 235, 495 ], "score": 1.0, "content": "maximum ASR at the electrode", "type": "text" }, { "bbox": [ 235, 483, 248, 493 ], "score": 0.6, "content": "\\mathbf { C } \\mathbf { z }", "type": "inline_equation" }, { "bbox": [ 249, 482, 505, 495 ], "score": 1.0, "content": "between the regions of the electrodes C3 and C4, which are the", "type": "text" } ], "index": 34 }, { "bbox": [ 106, 493, 505, 505 ], "spans": [ { "bbox": [ 106, 493, 505, 505 ], "score": 1.0, "content": "most relevant ones for MI of the left and right hand tasks (Pfurtscheller & Lopes da Silva, 1999). We", "type": "text" } ], "index": 35 }, { "bbox": [ 105, 504, 506, 516 ], "spans": [ { "bbox": [ 105, 504, 506, 516 ], "score": 1.0, "content": "compute the pre- and post-attack confusion matrices for attacks from T9 and T10 (see Appendix E).", "type": "text" } ], "index": 36 }, { "bbox": [ 105, 515, 505, 528 ], "spans": [ { "bbox": [ 105, 515, 505, 528 ], "score": 1.0, "content": "When the attack propagates from the left side (T9), more samples with ground-truth label “right” can", "type": "text" } ], "index": 37 }, { "bbox": [ 106, 527, 483, 538 ], "spans": [ { "bbox": [ 106, 527, 483, 538 ], "score": 1.0, "content": "be fooled to “rest”, while the attacks from the right side (T10) are more effective “left” labels.", "type": "text" } ], "index": 38 } ], "index": 34.5 }, { "type": "text", "bbox": [ 108, 543, 505, 587 ], "lines": [ { "bbox": [ 105, 542, 505, 556 ], "spans": [ { "bbox": [ 105, 542, 505, 556 ], "score": 1.0, "content": "Overall, our methods successfully generates perturbations resembling natural noise in EEGs, that can", "type": "text" } ], "index": 39 }, { "bbox": [ 105, 554, 506, 567 ], "spans": [ { "bbox": [ 105, 554, 506, 567 ], "score": 1.0, "content": "be added at the source of the signal acquisition and are propagated over the scalp, creating attacked", "type": "text" } ], "index": 40 }, { "bbox": [ 106, 566, 505, 578 ], "spans": [ { "bbox": [ 106, 566, 505, 578 ], "score": 1.0, "content": "signals that are physiologically plausible. Similar results have been observed on the BCI Competition", "type": "text" } ], "index": 41 }, { "bbox": [ 105, 576, 255, 587 ], "spans": [ { "bbox": [ 105, 576, 255, 587 ], "score": 1.0, "content": "IV-2a dataset, shown in Appendix C.", "type": "text" } ], "index": 42 } ], "index": 40.5 }, { "type": "title", "bbox": [ 108, 606, 195, 619 ], "lines": [ { "bbox": [ 104, 605, 197, 622 ], "spans": [ { "bbox": [ 104, 605, 197, 622 ], "score": 1.0, "content": "5 CONCLUSION", "type": "text" } ], "index": 43 } ], "index": 43 }, { "type": "text", "bbox": [ 107, 632, 506, 732 ], "lines": [ { "bbox": [ 106, 633, 505, 645 ], "spans": [ { "bbox": [ 106, 633, 505, 645 ], "score": 1.0, "content": "With the incentive of improving security in BCIs, in this work, we demonstrated that DoS attacks", "type": "text" } ], "index": 44 }, { "bbox": [ 106, 644, 505, 656 ], "spans": [ { "bbox": [ 106, 644, 505, 656 ], "score": 1.0, "content": "are feasible and effective despite physical domain constraints. Experimental results reveal potential", "type": "text" } ], "index": 45 }, { "bbox": [ 106, 655, 506, 667 ], "spans": [ { "bbox": [ 106, 655, 506, 667 ], "score": 1.0, "content": "risks of realistic attacks on smart wearable BCIs and incentivize the need for future development of", "type": "text" } ], "index": 46 }, { "bbox": [ 105, 665, 506, 678 ], "spans": [ { "bbox": [ 105, 665, 506, 678 ], "score": 1.0, "content": "defense mechanisms while designing deep learning models to be embedded in smart wearable BCIs.", "type": "text" } ], "index": 47 }, { "bbox": [ 105, 676, 506, 690 ], "spans": [ { "bbox": [ 105, 676, 506, 690 ], "score": 1.0, "content": "Our detailed analysis on each EEG channel shows that special attention has to be paid, combined", "type": "text" } ], "index": 48 }, { "bbox": [ 106, 688, 506, 700 ], "spans": [ { "bbox": [ 106, 688, 506, 700 ], "score": 1.0, "content": "with the findings in neuroscience, to the brain regions that are found responsible for a specific task.", "type": "text" } ], "index": 49 }, { "bbox": [ 105, 699, 506, 712 ], "spans": [ { "bbox": [ 105, 699, 506, 712 ], "score": 1.0, "content": "In future work, the proposed attacks can cover uncertainty in the propagation model and the timing of", "type": "text" } ], "index": 50 }, { "bbox": [ 106, 710, 505, 722 ], "spans": [ { "bbox": [ 106, 710, 505, 722 ], "score": 1.0, "content": "the MI activity. Moreover, hardware implementations of such attacks can be created to evaluate the", "type": "text" } ], "index": 51 }, { "bbox": [ 105, 721, 494, 733 ], "spans": [ { "bbox": [ 105, 721, 494, 733 ], "score": 1.0, "content": "proposed methods in real-world, with the ultimate goal of developing effective countermeasures.", "type": "text" } ], "index": 52 } ], "index": 48 } ], "page_idx": 8, "page_size": [ 612, 792 ], "discarded_blocks": [ { "type": "discarded", "bbox": [ 107, 27, 308, 37 ], "lines": [ { "bbox": [ 106, 25, 309, 39 ], "spans": [ { "bbox": [ 106, 25, 309, 39 ], "score": 1.0, "content": "Under review as a conference paper at ICLR 2022", "type": "text" } ] } ] }, { "type": "discarded", "bbox": [ 302, 751, 308, 759 ], "lines": [ { "bbox": [ 302, 751, 309, 762 ], "spans": [ { "bbox": [ 302, 751, 309, 762 ], "score": 1.0, "content": "9", "type": "text" } ] } ] } ], "para_blocks": [ { "type": "text", "bbox": [ 107, 82, 505, 203 ], "lines": [], "index": 5, "bbox_fs": [ 105, 83, 506, 206 ], "lines_deleted": true }, { "type": "text", "bbox": [ 107, 209, 505, 275 ], "lines": [ { "bbox": [ 106, 209, 505, 221 ], "spans": [ { "bbox": [ 106, 209, 505, 221 ], "score": 1.0, "content": "We extend the application of the derivative term to the UAP attack, while still not considering the", "type": "text" } ], "index": 11 }, { "bbox": [ 104, 219, 503, 233 ], "spans": [ { "bbox": [ 104, 219, 497, 233 ], "score": 1.0, "content": "propagation model (Case 1). Figure 1 shows a comparison in performance for different values of", "type": "text" }, { "bbox": [ 498, 222, 503, 230 ], "score": 0.59, "content": "\\epsilon", "type": "inline_equation" } ], "index": 12 }, { "bbox": [ 105, 230, 505, 243 ], "spans": [ { "bbox": [ 105, 230, 290, 243 ], "score": 1.0, "content": "The saturation in ASR is reached with higher", "type": "text" }, { "bbox": [ 290, 233, 296, 241 ], "score": 0.59, "content": "\\epsilon", "type": "inline_equation" }, { "bbox": [ 296, 230, 316, 243 ], "score": 1.0, "content": ", i.e.,", "type": "text" }, { "bbox": [ 317, 231, 349, 241 ], "score": 0.88, "content": "9 9 . 9 4 \\%", "type": "inline_equation" }, { "bbox": [ 349, 230, 370, 243 ], "score": 1.0, "content": "with", "type": "text" }, { "bbox": [ 370, 231, 397, 241 ], "score": 0.64, "content": "5 0 \\mathrm { m V } .", "type": "inline_equation" }, { "bbox": [ 397, 230, 505, 243 ], "score": 1.0, "content": ". This is expected since the", "type": "text" } ], "index": 13 }, { "bbox": [ 105, 241, 506, 254 ], "spans": [ { "bbox": [ 105, 241, 506, 254 ], "score": 1.0, "content": "UAP is a more difficult attack where a single set of perturbations per EEG channel is generated for", "type": "text" } ], "index": 14 }, { "bbox": [ 105, 252, 506, 265 ], "spans": [ { "bbox": [ 105, 252, 506, 265 ], "score": 1.0, "content": "all the test samples. Likewise in PGD, the ASR does not drop with the addition of the derivative term.", "type": "text" } ], "index": 15 }, { "bbox": [ 105, 263, 500, 276 ], "spans": [ { "bbox": [ 105, 263, 500, 276 ], "score": 1.0, "content": "We reproduce the UAP proposed by (Liu et al., 2021). Our UAP consistently reaches higher ASR.", "type": "text" } ], "index": 16 } ], "index": 13.5, "bbox_fs": [ 104, 209, 506, 276 ] }, { "type": "text", "bbox": [ 107, 290, 505, 444 ], "lines": [ { "bbox": [ 106, 290, 505, 303 ], "spans": [ { "bbox": [ 106, 290, 505, 303 ], "score": 1.0, "content": "Spatial Propagation. Finally, we introduce the spatial constraints in the signal propagation over the", "type": "text" } ], "index": 17 }, { "bbox": [ 106, 301, 505, 314 ], "spans": [ { "bbox": [ 106, 301, 505, 314 ], "score": 1.0, "content": "scalp (Case 2). We consider 9 different scenarios by combining 3 realistic attenuation configurations", "type": "text" } ], "index": 18 }, { "bbox": [ 107, 309, 506, 327 ], "spans": [ { "bbox": [ 107, 312, 177, 324 ], "score": 0.91, "content": "\\lambda _ { m } \\ \\in \\ \\{ 1 , 5 , 1 5 \\}", "type": "inline_equation" }, { "bbox": [ 178, 309, 299, 327 ], "score": 1.0, "content": "with 3 delay configurations", "type": "text" }, { "bbox": [ 299, 312, 395, 324 ], "score": 0.93, "content": "\\lambda _ { d } \\ \\in \\ \\{ 0 . 1 , 0 . 3 , 0 . 5 6 3 \\}", "type": "inline_equation" }, { "bbox": [ 396, 309, 506, 327 ], "score": 1.0, "content": ", which capture the range", "type": "text" } ], "index": 19 }, { "bbox": [ 105, 322, 505, 336 ], "spans": [ { "bbox": [ 105, 322, 505, 336 ], "score": 1.0, "content": "described in Section 3.2. For evaluating the highest achievable attack efficiency, we test a scenario", "type": "text" } ], "index": 20 }, { "bbox": [ 106, 334, 506, 347 ], "spans": [ { "bbox": [ 106, 334, 506, 347 ], "score": 1.0, "content": "where the attacker is assumed to know the propagation model: the adversarial perturbation is generated", "type": "text" } ], "index": 21 }, { "bbox": [ 105, 344, 505, 358 ], "spans": [ { "bbox": [ 105, 344, 272, 358 ], "score": 1.0, "content": "and evaluated on fixed spatial parameters", "type": "text" }, { "bbox": [ 272, 345, 287, 356 ], "score": 0.9, "content": "\\lambda _ { m }", "type": "inline_equation" }, { "bbox": [ 287, 344, 304, 358 ], "score": 1.0, "content": "and", "type": "text" }, { "bbox": [ 305, 345, 317, 356 ], "score": 0.88, "content": "\\lambda _ { d }", "type": "inline_equation" }, { "bbox": [ 317, 344, 505, 358 ], "score": 1.0, "content": ", shown in Figure 4, where the ASR reaches up", "type": "text" } ], "index": 22 }, { "bbox": [ 106, 355, 506, 368 ], "spans": [ { "bbox": [ 106, 355, 116, 368 ], "score": 1.0, "content": "to", "type": "text" }, { "bbox": [ 117, 356, 143, 366 ], "score": 0.87, "content": "6 9 . 2 \\%", "type": "inline_equation" }, { "bbox": [ 144, 355, 202, 368 ], "score": 1.0, "content": "with PGD and", "type": "text" }, { "bbox": [ 202, 356, 228, 366 ], "score": 0.86, "content": "4 5 . 6 \\%", "type": "inline_equation" }, { "bbox": [ 229, 355, 280, 368 ], "score": 1.0, "content": "with UAP at", "type": "text" }, { "bbox": [ 280, 356, 306, 366 ], "score": 0.61, "content": "5 0 \\mathrm { m V } .", "type": "inline_equation" }, { "bbox": [ 306, 355, 506, 368 ], "score": 1.0, "content": "Figure 3 depicts an example of a successful attack", "type": "text" } ], "index": 23 }, { "bbox": [ 106, 367, 506, 380 ], "spans": [ { "bbox": [ 106, 367, 506, 380 ], "score": 1.0, "content": "with the highest perturbation amplitude. The introduction of the spatial constraints makes the attack", "type": "text" } ], "index": 24 }, { "bbox": [ 105, 377, 506, 390 ], "spans": [ { "bbox": [ 105, 377, 506, 390 ], "score": 1.0, "content": "problem harder yielding seldom square distortions. However, the resulting EEG signals still resemble", "type": "text" } ], "index": 25 }, { "bbox": [ 105, 388, 506, 403 ], "spans": [ { "bbox": [ 105, 388, 506, 403 ], "score": 1.0, "content": "physiological random processes typical of EEGs. Next, we ablate the spatial constraints during", "type": "text" } ], "index": 26 }, { "bbox": [ 105, 399, 506, 413 ], "spans": [ { "bbox": [ 105, 399, 506, 413 ], "score": 1.0, "content": "generation and test the resulting perturbations on the 9 above-mentioned scenarios (Case 3). The", "type": "text" } ], "index": 27 }, { "bbox": [ 105, 410, 505, 423 ], "spans": [ { "bbox": [ 105, 410, 263, 423 ], "score": 1.0, "content": "ASR drops significantly, especially for", "type": "text" }, { "bbox": [ 264, 411, 289, 422 ], "score": 0.91, "content": "\\lambda _ { m } { = } 5", "type": "inline_equation" }, { "bbox": [ 290, 410, 307, 423 ], "score": 1.0, "content": "and", "type": "text" }, { "bbox": [ 307, 411, 338, 422 ], "score": 0.91, "content": "\\lambda _ { m } { = } 1 5", "type": "inline_equation" }, { "bbox": [ 338, 410, 505, 423 ], "score": 1.0, "content": "where the attenuation of the perturbation", "type": "text" } ], "index": 28 }, { "bbox": [ 105, 421, 506, 434 ], "spans": [ { "bbox": [ 105, 421, 506, 434 ], "score": 1.0, "content": "over the scalp is greater (see Figure 7), and with the global UAP attack, where the attacker does not", "type": "text" } ], "index": 29 }, { "bbox": [ 106, 433, 270, 445 ], "spans": [ { "bbox": [ 106, 433, 270, 445 ], "score": 1.0, "content": "have access to the attacked EEG signals.", "type": "text" } ], "index": 30 } ], "index": 23.5, "bbox_fs": [ 105, 290, 506, 445 ] }, { "type": "text", "bbox": [ 107, 450, 505, 538 ], "lines": [ { "bbox": [ 106, 450, 506, 462 ], "spans": [ { "bbox": [ 106, 450, 506, 462 ], "score": 1.0, "content": "Our spatial propagation models allows us to identify the vulnerability of the individual EEG channels.", "type": "text" } ], "index": 31 }, { "bbox": [ 105, 460, 505, 473 ], "spans": [ { "bbox": [ 105, 460, 505, 473 ], "score": 1.0, "content": "Figure 5 shows the ASR when initiating an attack from a specific channel (T9, T10, etc.) and", "type": "text" } ], "index": 32 }, { "bbox": [ 105, 471, 506, 484 ], "spans": [ { "bbox": [ 105, 471, 421, 484 ], "score": 1.0, "content": "propagating it to the rest of the head. 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We", "type": "text" } ], "index": 35 }, { "bbox": [ 105, 504, 506, 516 ], "spans": [ { "bbox": [ 105, 504, 506, 516 ], "score": 1.0, "content": "compute the pre- and post-attack confusion matrices for attacks from T9 and T10 (see Appendix E).", "type": "text" } ], "index": 36 }, { "bbox": [ 105, 515, 505, 528 ], "spans": [ { "bbox": [ 105, 515, 505, 528 ], "score": 1.0, "content": "When the attack propagates from the left side (T9), more samples with ground-truth label “right” can", "type": "text" } ], "index": 37 }, { "bbox": [ 106, 527, 483, 538 ], "spans": [ { "bbox": [ 106, 527, 483, 538 ], "score": 1.0, "content": "be fooled to “rest”, while the attacks from the right side (T10) are more effective “left” labels.", "type": "text" } ], "index": 38 } ], "index": 34.5, "bbox_fs": [ 105, 450, 506, 538 ] }, { "type": "text", "bbox": [ 108, 543, 505, 587 ], "lines": [ { "bbox": [ 105, 542, 505, 556 ], "spans": [ { "bbox": [ 105, 542, 505, 556 ], "score": 1.0, "content": "Overall, our methods successfully generates perturbations resembling natural noise in EEGs, that can", "type": "text" } ], "index": 39 }, { "bbox": [ 105, 554, 506, 567 ], "spans": [ { "bbox": [ 105, 554, 506, 567 ], "score": 1.0, "content": "be added at the source of the signal acquisition and are propagated over the scalp, creating attacked", "type": "text" } ], "index": 40 }, { "bbox": [ 106, 566, 505, 578 ], "spans": [ { "bbox": [ 106, 566, 505, 578 ], "score": 1.0, "content": "signals that are physiologically plausible. 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The learning rate is 0.001 achieving", "type": "text" } ], "index": 29 }, { "bbox": [ 105, 631, 505, 644 ], "spans": [ { "bbox": [ 105, 631, 199, 644 ], "score": 1.0, "content": "an average accuracy of", "type": "text" }, { "bbox": [ 200, 632, 231, 642 ], "score": 0.88, "content": "7 1 . 7 9 \\%", "type": "inline_equation" }, { "bbox": [ 231, 631, 505, 644 ], "score": 1.0, "content": ". This dataset does not contain the rest class. 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A large", "type": "text" }, { "bbox": [ 183, 457, 198, 468 ], "score": 0.89, "content": "\\lambda _ { m }", "type": "inline_equation" }, { "bbox": [ 198, 457, 506, 470 ], "score": 1.0, "content": "represents cases with large attenuation and limited propagation (e.g., attack", "type": "text" } ], "index": 18 }, { "bbox": [ 105, 468, 505, 481 ], "spans": [ { "bbox": [ 105, 468, 202, 481 ], "score": 1.0, "content": "over the air) and a small", "type": "text" }, { "bbox": [ 202, 469, 217, 479 ], "score": 0.89, "content": "\\lambda _ { m }", "type": "inline_equation" }, { "bbox": [ 217, 468, 505, 481 ], "score": 1.0, "content": "covers cases with lower attenuation where the perturbation can propagate", "type": "text" } ], "index": 19 }, { "bbox": [ 105, 478, 218, 491 ], "spans": [ { "bbox": [ 105, 478, 218, 491 ], "score": 1.0, "content": "farther (e.g., a smart glass).", "type": "text" } ], "index": 20 } ], "index": 18.5, "bbox_fs": [ 105, 445, 506, 491 ] }, { "type": "title", "bbox": [ 107, 506, 355, 519 ], "lines": [ { "bbox": [ 106, 506, 356, 520 ], "spans": [ { "bbox": [ 106, 506, 356, 520 ], "score": 1.0, "content": "C EXPERIMENTS ON BCI COMPETITION IV-2A", "type": "text" } ], "index": 21 } ], "index": 21 }, { "type": "text", "bbox": [ 107, 531, 505, 597 ], "lines": [ { "bbox": [ 106, 531, 505, 543 ], "spans": [ { "bbox": [ 106, 531, 505, 543 ], "score": 1.0, "content": "Dataset. The IV-2a dataset of the BCI Competition contains recordings from nine different subjects", "type": "text" } ], "index": 22 }, { "bbox": [ 106, 543, 505, 554 ], "spans": [ { "bbox": [ 106, 543, 505, 554 ], "score": 1.0, "content": "and distinguishes between four classes of imagined movements: left and right hand, both feet, and the", "type": "text" } ], "index": 23 }, { "bbox": [ 106, 553, 506, 565 ], "spans": [ { "bbox": [ 106, 553, 357, 565 ], "score": 1.0, "content": "tongue. 22 different EEG channels were recorded, sampled at", "type": "text" }, { "bbox": [ 357, 553, 387, 564 ], "score": 0.68, "content": "2 5 0 \\mathrm { H z }", "type": "inline_equation" }, { "bbox": [ 387, 553, 506, 565 ], "score": 1.0, "content": ". The data was pre-processed", "type": "text" } ], "index": 24 }, { "bbox": [ 106, 564, 505, 577 ], "spans": [ { "bbox": [ 106, 564, 260, 577 ], "score": 1.0, "content": "with a bandpass filter between 0.1 and", "type": "text" }, { "bbox": [ 261, 564, 285, 574 ], "score": 0.76, "content": "4 0 \\mathrm { H z }", "type": "inline_equation" }, { "bbox": [ 286, 564, 505, 577 ], "score": 1.0, "content": ". Each subject completed two recording session on two", "type": "text" } ], "index": 25 }, { "bbox": [ 105, 574, 506, 587 ], "spans": [ { "bbox": [ 105, 574, 506, 587 ], "score": 1.0, "content": "different days, where the first session is used for training and the second for testing as per the rules of", "type": "text" } ], "index": 26 }, { "bbox": [ 106, 586, 306, 597 ], "spans": [ { "bbox": [ 106, 586, 306, 597 ], "score": 1.0, "content": "the competition. Each session contains 288 trials.", "type": "text" } ], "index": 27 } ], "index": 24.5, "bbox_fs": [ 105, 531, 506, 597 ] }, { "type": "text", "bbox": [ 107, 609, 505, 675 ], "lines": [ { "bbox": [ 106, 609, 506, 622 ], "spans": [ { "bbox": [ 106, 609, 506, 622 ], "score": 1.0, "content": "Training and validation. We train a separate baseline model per subject using Adam optimizer", "type": "text" } ], "index": 28 }, { "bbox": [ 105, 619, 506, 634 ], "spans": [ { "bbox": [ 105, 619, 126, 634 ], "score": 1.0, "content": "with", "type": "text" }, { "bbox": [ 126, 621, 156, 632 ], "score": 0.91, "content": "\\beta _ { 1 } { = } 0 . 9", "type": "inline_equation" }, { "bbox": [ 156, 619, 173, 634 ], "score": 1.0, "content": "and", "type": "text" }, { "bbox": [ 174, 621, 213, 632 ], "score": 0.89, "content": "\\beta _ { 2 } { = } 0 . 9 9 9", "type": "inline_equation" }, { "bbox": [ 213, 619, 506, 634 ], "score": 1.0, "content": ", a batch size of 32, and 500 epochs. The learning rate is 0.001 achieving", "type": "text" } ], "index": 29 }, { "bbox": [ 105, 631, 505, 644 ], "spans": [ { "bbox": [ 105, 631, 199, 644 ], "score": 1.0, "content": "an average accuracy of", "type": "text" }, { "bbox": [ 200, 632, 231, 642 ], "score": 0.88, "content": "7 1 . 7 9 \\%", "type": "inline_equation" }, { "bbox": [ 231, 631, 505, 644 ], "score": 1.0, "content": ". This dataset does not contain the rest class. We choose to design an", "type": "text" } ], "index": 30 }, { "bbox": [ 105, 642, 506, 655 ], "spans": [ { "bbox": [ 105, 642, 506, 655 ], "score": 1.0, "content": "attack that aims to fool the classifier to always predict “tongue.” Moreover, we apply a maximum", "type": "text" } ], "index": 31 }, { "bbox": [ 105, 653, 506, 666 ], "spans": [ { "bbox": [ 105, 653, 213, 666 ], "score": 1.0, "content": "perturbation amplitude of", "type": "text" }, { "bbox": [ 213, 653, 285, 665 ], "score": 0.91, "content": "\\epsilon \\in [ 0 . 0 1 , 1 0 ] \\mathrm { m V }", "type": "inline_equation" }, { "bbox": [ 286, 653, 506, 666 ], "score": 1.0, "content": "due to the lower signal amplitude encountered in this", "type": "text" } ], "index": 32 }, { "bbox": [ 105, 663, 140, 677 ], "spans": [ { "bbox": [ 105, 663, 140, 677 ], "score": 1.0, "content": "dataset.", "type": "text" } ], "index": 33 } ], "index": 30.5, "bbox_fs": [ 105, 609, 506, 677 ] }, { "type": "text", "bbox": [ 107, 687, 504, 732 ], "lines": [ { "bbox": [ 105, 687, 505, 700 ], "spans": [ { "bbox": [ 105, 687, 505, 700 ], "score": 1.0, "content": "Results. Fig. 8 compares the ASR of different attacks without considering the propagation model", "type": "text" } ], "index": 34 }, { "bbox": [ 105, 699, 505, 711 ], "spans": [ { "bbox": [ 105, 699, 338, 711 ], "score": 1.0, "content": "(Case 1). Generally, a minimal perturbation amplitude of", "type": "text" }, { "bbox": [ 339, 699, 361, 709 ], "score": 0.65, "content": "1 \\mathrm { m V }", "type": "inline_equation" }, { "bbox": [ 362, 699, 379, 711 ], "score": 1.0, "content": "and", "type": "text" }, { "bbox": [ 379, 699, 403, 709 ], "score": 0.69, "content": "2 \\mathrm { m V }", "type": "inline_equation" }, { "bbox": [ 403, 699, 480, 711 ], "score": 1.0, "content": "suffices to achieve", "type": "text" }, { "bbox": [ 480, 699, 505, 709 ], "score": 0.85, "content": "100 \\%", "type": "inline_equation" } ], "index": 35 }, { "bbox": [ 105, 709, 506, 723 ], "spans": [ { "bbox": [ 105, 709, 506, 723 ], "score": 1.0, "content": "ASR with PGD and UAP, respectively. The addition of the derivative loss term does not give any", "type": "text" } ], "index": 36 }, { "bbox": [ 105, 720, 506, 734 ], "spans": [ { "bbox": [ 105, 720, 506, 734 ], "score": 1.0, "content": "performance degradation in terms of the ASR. The average post-attack classification accuracy drops", "type": "text" } ], "index": 37 }, { "bbox": [ 105, 332, 504, 347 ], "spans": [ { "bbox": [ 105, 332, 129, 347 ], "score": 1.0, "content": "from", "type": "text", "cross_page": true }, { "bbox": [ 129, 334, 162, 344 ], "score": 0.86, "content": "7 1 . 7 9 \\%", "type": "inline_equation", "cross_page": true }, { "bbox": [ 162, 332, 174, 347 ], "score": 1.0, "content": "to", "type": "text", "cross_page": true }, { "bbox": [ 174, 334, 195, 344 ], "score": 0.86, "content": "50 \\%", "type": "inline_equation", "cross_page": true }, { "bbox": [ 195, 332, 326, 347 ], "score": 1.0, "content": "for a perturbation amplitude of", "type": "text", "cross_page": true }, { "bbox": [ 327, 334, 363, 344 ], "score": 0.8, "content": "0 . 1 5 \\mathrm { m V }", "type": "inline_equation", "cross_page": true }, { "bbox": [ 363, 332, 382, 347 ], "score": 1.0, "content": "and", "type": "text", "cross_page": true }, { "bbox": [ 382, 334, 410, 344 ], "score": 0.86, "content": "2 4 . 7 \\%", "type": "inline_equation", "cross_page": true }, { "bbox": [ 410, 332, 426, 347 ], "score": 1.0, "content": "for", "type": "text", "cross_page": true }, { "bbox": [ 426, 334, 457, 344 ], "score": 0.82, "content": "0 . 6 \\mathrm { m V }", "type": "inline_equation", "cross_page": true }, { "bbox": [ 458, 332, 504, 347 ], "score": 1.0, "content": "and higher", "type": "text", "cross_page": true } ], "index": 5 }, { "bbox": [ 106, 344, 295, 357 ], "spans": [ { "bbox": [ 106, 344, 295, 357 ], "score": 1.0, "content": "amplitudes, when PGD with derivative is used.", "type": "text", "cross_page": true } ], "index": 6 } ], "index": 35.5, "bbox_fs": [ 105, 687, 506, 734 ] } ] }, { "preproc_blocks": [ { "type": "image", "bbox": [ 111, 80, 495, 262 ], "blocks": [ { "type": "image_body", "bbox": [ 111, 80, 495, 262 ], "group_id": 0, "lines": [ { "bbox": [ 111, 80, 495, 262 ], "spans": [ { "bbox": [ 111, 80, 495, 262 ], "score": 0.784, "type": "image", "image_path": "2754d89a4671045e54c9d9a042e28c5bfceebb00058d60cea27bd8868f79baae.jpg" } ] } ], "index": 1, "virtual_lines": [ { "bbox": [ 111, 80, 495, 140.66666666666666 ], "spans": [], "index": 0 }, { "bbox": [ 111, 140.66666666666666, 495, 201.33333333333331 ], "spans": [], "index": 1 }, { "bbox": [ 111, 201.33333333333331, 495, 262.0 ], "spans": [], "index": 2 } ] }, { "type": "image_caption", "bbox": [ 105, 273, 504, 295 ], "group_id": 0, "lines": [ { "bbox": [ 105, 273, 506, 285 ], "spans": [ { "bbox": [ 105, 273, 506, 285 ], "score": 1.0, "content": "Figure 8: ASR on BCI Competition IV-2a with random noise, FGSM, PGD, and UAP with and without", "type": "text" } ], "index": 3 }, { "bbox": [ 105, 283, 181, 295 ], "spans": [ { "bbox": [ 105, 283, 181, 295 ], "score": 1.0, "content": "derivative loss term.", "type": "text" } ], "index": 4 } ], "index": 3.5 } ], "index": 2.25 }, { "type": "text", "bbox": [ 107, 333, 502, 356 ], "lines": [ { "bbox": [ 105, 332, 504, 347 ], "spans": [ { "bbox": [ 105, 332, 129, 347 ], "score": 1.0, "content": "from", "type": "text" }, { "bbox": [ 129, 334, 162, 344 ], "score": 0.86, "content": "7 1 . 7 9 \\%", "type": "inline_equation" }, { "bbox": [ 162, 332, 174, 347 ], "score": 1.0, "content": "to", "type": "text" }, { "bbox": [ 174, 334, 195, 344 ], "score": 0.86, "content": "50 \\%", "type": "inline_equation" }, { "bbox": [ 195, 332, 326, 347 ], "score": 1.0, "content": "for a perturbation amplitude of", "type": "text" }, { "bbox": [ 327, 334, 363, 344 ], "score": 0.8, "content": "0 . 1 5 \\mathrm { m V }", "type": "inline_equation" }, { "bbox": [ 363, 332, 382, 347 ], "score": 1.0, "content": "and", "type": "text" }, { "bbox": [ 382, 334, 410, 344 ], "score": 0.86, "content": "2 4 . 7 \\%", "type": "inline_equation" }, { "bbox": [ 410, 332, 426, 347 ], "score": 1.0, "content": "for", "type": "text" }, { "bbox": [ 426, 334, 457, 344 ], "score": 0.82, "content": "0 . 6 \\mathrm { m V }", "type": "inline_equation" }, { "bbox": [ 458, 332, 504, 347 ], "score": 1.0, "content": "and higher", "type": "text" } ], "index": 5 }, { "bbox": [ 106, 344, 295, 357 ], "spans": [ { "bbox": [ 106, 344, 295, 357 ], "score": 1.0, "content": "amplitudes, when PGD with derivative is used.", "type": "text" } ], "index": 6 } ], "index": 5.5 }, { "type": "text", "bbox": [ 107, 361, 505, 405 ], "lines": [ { "bbox": [ 105, 361, 505, 374 ], "spans": [ { "bbox": [ 105, 361, 345, 374 ], "score": 1.0, "content": "Fig. 9 shows the ASR for different propagation parameters", "type": "text" }, { "bbox": [ 345, 362, 360, 372 ], "score": 0.88, "content": "\\lambda _ { m }", "type": "inline_equation" }, { "bbox": [ 361, 361, 378, 374 ], "score": 1.0, "content": "and", "type": "text" }, { "bbox": [ 378, 362, 390, 372 ], "score": 0.84, "content": "\\lambda _ { d , }", "type": "inline_equation" }, { "bbox": [ 391, 361, 505, 374 ], "score": 1.0, "content": ") and maximum perturbation", "type": "text" } ], "index": 7 }, { "bbox": [ 105, 372, 505, 384 ], "spans": [ { "bbox": [ 105, 372, 151, 384 ], "score": 1.0, "content": "amplitudes", "type": "text" }, { "bbox": [ 151, 374, 157, 382 ], "score": 0.53, "content": "\\epsilon", "type": "inline_equation" }, { "bbox": [ 158, 372, 505, 384 ], "score": 1.0, "content": ". When considering the head model during the design of the attack (Case 2, w/HM), both", "type": "text" } ], "index": 8 }, { "bbox": [ 105, 383, 505, 396 ], "spans": [ { "bbox": [ 105, 383, 505, 396 ], "score": 1.0, "content": "PGD and UAP reach significantly higher ASR compared to attacks designed without the consideration", "type": "text" } ], "index": 9 }, { "bbox": [ 105, 394, 255, 406 ], "spans": [ { "bbox": [ 105, 394, 255, 406 ], "score": 1.0, "content": "of the head model (Case 3, w/oHM).", "type": "text" } ], "index": 10 } ], "index": 8.5 }, { "type": "title", "bbox": [ 108, 442, 266, 454 ], "lines": [ { "bbox": [ 105, 441, 268, 456 ], "spans": [ { "bbox": [ 105, 441, 268, 456 ], "score": 1.0, "content": "D PLAUSIBILITY OF ATTACKS", "type": "text" } ], "index": 11 } ], "index": 11 }, { "type": "text", "bbox": [ 106, 478, 505, 567 ], "lines": [ { "bbox": [ 105, 478, 505, 492 ], "spans": [ { "bbox": [ 105, 478, 505, 492 ], "score": 1.0, "content": "This section provides power spectral density plots of original signals and attacked signals with and", "type": "text" } ], "index": 12 }, { "bbox": [ 106, 489, 505, 501 ], "spans": [ { "bbox": [ 106, 489, 505, 501 ], "score": 1.0, "content": "without the derivative loss term, shown in Figure 10. The power spectral density is determined", "type": "text" } ], "index": 13 }, { "bbox": [ 106, 501, 505, 513 ], "spans": [ { "bbox": [ 106, 501, 505, 513 ], "score": 1.0, "content": "by computing the magnitude squared Fast Fourier Transform of the signals that were illustrated in", "type": "text" } ], "index": 14 }, { "bbox": [ 105, 511, 505, 524 ], "spans": [ { "bbox": [ 105, 511, 505, 524 ], "score": 1.0, "content": "Figure 2. The attack designed with the derivative loss term has a similar distribution as the original", "type": "text" } ], "index": 15 }, { "bbox": [ 105, 523, 505, 535 ], "spans": [ { "bbox": [ 105, 523, 505, 535 ], "score": 1.0, "content": "signal, where as the attack without derivative shows large contributions in the low frequency domain", "type": "text" } ], "index": 16 }, { "bbox": [ 107, 533, 505, 546 ], "spans": [ { "bbox": [ 107, 533, 139, 545 ], "score": 0.85, "content": "( < 5 \\mathrm { H z } )", "type": "inline_equation" }, { "bbox": [ 139, 533, 505, 546 ], "score": 1.0, "content": ", which were not present in the original signal. These low-frequency components stem from", "type": "text" } ], "index": 17 }, { "bbox": [ 106, 545, 506, 557 ], "spans": [ { "bbox": [ 106, 545, 506, 557 ], "score": 1.0, "content": "the square-wave shaped attack and can be used as a way to detect the attack; hence, this attack cannot", "type": "text" } ], "index": 18 }, { "bbox": [ 106, 556, 224, 568 ], "spans": [ { "bbox": [ 106, 556, 224, 568 ], "score": 1.0, "content": "be considered imperceptible.", "type": "text" } ], "index": 19 } ], "index": 15.5 }, { "type": "text", "bbox": [ 106, 572, 505, 671 ], "lines": [ { "bbox": [ 106, 572, 505, 584 ], "spans": [ { "bbox": [ 106, 572, 505, 584 ], "score": 1.0, "content": "Moreover, Figure 13 shows the attacks with and without derivative loss term with increasing maximum", "type": "text" } ], "index": 20 }, { "bbox": [ 105, 582, 505, 595 ], "spans": [ { "bbox": [ 105, 582, 309, 595 ], "score": 1.0, "content": "amplitude \u000f. We can see that for low amplitudes (", "type": "text" }, { "bbox": [ 309, 583, 333, 594 ], "score": 0.56, "content": "\\mathrm { 1 m V }", "type": "inline_equation" }, { "bbox": [ 333, 582, 351, 595 ], "score": 1.0, "content": "and", "type": "text" }, { "bbox": [ 351, 583, 375, 594 ], "score": 0.47, "content": "5 \\mathrm { m V }", "type": "inline_equation" }, { "bbox": [ 375, 582, 505, 595 ], "score": 1.0, "content": ") the generated attacks with and", "type": "text" } ], "index": 21 }, { "bbox": [ 105, 593, 505, 607 ], "spans": [ { "bbox": [ 105, 593, 280, 607 ], "score": 1.0, "content": "without derivative still look like EEGs. At", "type": "text" }, { "bbox": [ 280, 594, 307, 605 ], "score": 0.7, "content": "1 0 \\mathrm { m V } ,", "type": "inline_equation" }, { "bbox": [ 308, 593, 505, 607 ], "score": 1.0, "content": "the attack generated without derivative presents", "type": "text" } ], "index": 22 }, { "bbox": [ 105, 604, 506, 618 ], "spans": [ { "bbox": [ 105, 604, 457, 618 ], "score": 1.0, "content": "minor square-wave artifacts, which could be still imperceptible to a non-expert. With", "type": "text" }, { "bbox": [ 458, 605, 487, 615 ], "score": 0.73, "content": "2 5 \\mathrm { m V }", "type": "inline_equation" }, { "bbox": [ 487, 604, 506, 618 ], "score": 1.0, "content": "and", "type": "text" } ], "index": 23 }, { "bbox": [ 106, 615, 506, 629 ], "spans": [ { "bbox": [ 106, 616, 134, 627 ], "score": 0.7, "content": "5 0 \\mathrm { m V } ,", "type": "inline_equation" }, { "bbox": [ 134, 615, 506, 629 ], "score": 1.0, "content": ", the ones generated without derivative have strong and perceptible square-wave displacements,", "type": "text" } ], "index": 24 }, { "bbox": [ 106, 627, 505, 639 ], "spans": [ { "bbox": [ 106, 627, 505, 639 ], "score": 1.0, "content": "while the ones generated with our proposed method can still be mistaken as real EEG signals. While", "type": "text" } ], "index": 25 }, { "bbox": [ 106, 638, 504, 650 ], "spans": [ { "bbox": [ 106, 638, 382, 650 ], "score": 1.0, "content": "with the instance-based attacks, it is not necessary to have more than", "type": "text" }, { "bbox": [ 382, 638, 410, 648 ], "score": 0.75, "content": "1 0 \\mathrm { m V }", "type": "inline_equation" }, { "bbox": [ 410, 638, 504, 650 ], "score": 1.0, "content": "to get a very high ASR", "type": "text" } ], "index": 26 }, { "bbox": [ 104, 647, 506, 663 ], "spans": [ { "bbox": [ 104, 647, 506, 663 ], "score": 1.0, "content": "(see Figure 1), with the universal attacks and physical constraints, the ASR increases with increasing", "type": "text" } ], "index": 27 }, { "bbox": [ 105, 660, 260, 673 ], "spans": [ { "bbox": [ 105, 660, 260, 673 ], "score": 1.0, "content": "perturbation amplitude (see Figure 4).", "type": "text" } ], "index": 28 } ], "index": 24 }, { "type": "text", "bbox": [ 107, 677, 505, 732 ], "lines": [ { "bbox": [ 106, 677, 505, 689 ], "spans": [ { "bbox": [ 106, 677, 505, 689 ], "score": 1.0, "content": "The same observations can be drawn from the plausibility metrics, which have been proposed for the", "type": "text" } ], "index": 29 }, { "bbox": [ 106, 688, 505, 700 ], "spans": [ { "bbox": [ 106, 688, 505, 700 ], "score": 1.0, "content": "first time in this paper to assess quantitatively the EEG attacks. For example, looking at the cosine", "type": "text" } ], "index": 30 }, { "bbox": [ 105, 697, 507, 713 ], "spans": [ { "bbox": [ 105, 697, 147, 713 ], "score": 1.0, "content": "similarity", "type": "text" }, { "bbox": [ 147, 699, 160, 710 ], "score": 0.8, "content": "( \\gamma )", "type": "inline_equation" }, { "bbox": [ 161, 697, 335, 713 ], "score": 1.0, "content": "in Table 1, without the derivative loss term,", "type": "text" }, { "bbox": [ 335, 700, 343, 710 ], "score": 0.8, "content": "\\gamma", "type": "inline_equation" }, { "bbox": [ 343, 697, 378, 713 ], "score": 1.0, "content": "drops to", "type": "text" }, { "bbox": [ 378, 699, 410, 709 ], "score": 0.88, "content": "9 7 . 9 9 \\%", "type": "inline_equation" }, { "bbox": [ 411, 697, 431, 713 ], "score": 1.0, "content": "with", "type": "text" }, { "bbox": [ 432, 699, 466, 709 ], "score": 0.89, "content": "\\epsilon = 5 \\mathrm { m V } ,", "type": "inline_equation" }, { "bbox": [ 466, 697, 507, 713 ], "score": 1.0, "content": ", whereas,", "type": "text" } ], "index": 31 }, { "bbox": [ 105, 708, 505, 723 ], "spans": [ { "bbox": [ 105, 708, 184, 723 ], "score": 1.0, "content": "with the derivative,", "type": "text" }, { "bbox": [ 185, 711, 192, 721 ], "score": 0.8, "content": "\\gamma", "type": "inline_equation" }, { "bbox": [ 192, 708, 324, 723 ], "score": 1.0, "content": "drops to about the same value of", "type": "text" }, { "bbox": [ 325, 710, 357, 720 ], "score": 0.88, "content": "9 7 . 4 7 \\%", "type": "inline_equation" }, { "bbox": [ 357, 708, 378, 723 ], "score": 1.0, "content": "with", "type": "text" }, { "bbox": [ 378, 710, 420, 720 ], "score": 0.89, "content": "\\epsilon = 1 0 \\mathrm { m V } ,", "type": "inline_equation" }, { "bbox": [ 421, 708, 505, 723 ], "score": 1.0, "content": "yielding an increase", "type": "text" } ], "index": 32 }, { "bbox": [ 105, 720, 398, 733 ], "spans": [ { "bbox": [ 105, 720, 160, 733 ], "score": 1.0, "content": "in ASR from", "type": "text" }, { "bbox": [ 160, 721, 180, 731 ], "score": 0.82, "content": "85 \\%", "type": "inline_equation" }, { "bbox": [ 180, 720, 184, 733 ], "score": 0.0, "content": "", "type": "text" }, { "bbox": [ 184, 721, 209, 731 ], "score": 0.41, "content": "\\mathrm { 5 m V ) }", "type": "inline_equation" }, { "bbox": [ 210, 720, 222, 733 ], "score": 1.0, "content": "to", "type": "text" }, { "bbox": [ 222, 721, 242, 731 ], "score": 0.83, "content": "9 9 \\%", "type": "inline_equation" }, { "bbox": [ 242, 720, 246, 733 ], "score": 0.0, "content": "", "type": "text" }, { "bbox": [ 246, 721, 276, 731 ], "score": 0.62, "content": "\\mathrm { 1 0 m V ) }", "type": "inline_equation" }, { "bbox": [ 276, 720, 398, 733 ], "score": 1.0, "content": "shown in Figure 1 with PGD.", "type": "text" } ], "index": 33 } ], "index": 31 } ], "page_idx": 14, "page_size": [ 612, 792 ], "discarded_blocks": [ { "type": "discarded", "bbox": [ 106, 27, 308, 37 ], "lines": [ { "bbox": [ 106, 26, 309, 38 ], "spans": [ { "bbox": [ 106, 26, 309, 38 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When considering the head model during the design of the attack (Case 2, w/HM), both", "type": "text" } ], "index": 8 }, { "bbox": [ 105, 383, 505, 396 ], "spans": [ { "bbox": [ 105, 383, 505, 396 ], "score": 1.0, "content": "PGD and UAP reach significantly higher ASR compared to attacks designed without the consideration", "type": "text" } ], "index": 9 }, { "bbox": [ 105, 394, 255, 406 ], "spans": [ { "bbox": [ 105, 394, 255, 406 ], "score": 1.0, "content": "of the head model (Case 3, w/oHM).", "type": "text" } ], "index": 10 } ], "index": 8.5, "bbox_fs": [ 105, 361, 505, 406 ] }, { "type": "title", "bbox": [ 108, 442, 266, 454 ], "lines": [ { "bbox": [ 105, 441, 268, 456 ], "spans": [ { "bbox": [ 105, 441, 268, 456 ], "score": 1.0, "content": "D PLAUSIBILITY OF ATTACKS", "type": "text" } ], "index": 11 } ], "index": 11 }, { "type": "text", "bbox": [ 106, 478, 505, 567 ], "lines": [ { "bbox": [ 105, 478, 505, 492 ], "spans": [ { "bbox": [ 105, 478, 505, 492 ], "score": 1.0, "content": "This section provides power spectral density plots of original signals and attacked signals with and", "type": "text" } ], "index": 12 }, { "bbox": [ 106, 489, 505, 501 ], "spans": [ { "bbox": [ 106, 489, 505, 501 ], "score": 1.0, "content": "without the derivative loss term, shown in Figure 10. The power spectral density is determined", "type": "text" } ], "index": 13 }, { "bbox": [ 106, 501, 505, 513 ], "spans": [ { "bbox": [ 106, 501, 505, 513 ], "score": 1.0, "content": "by computing the magnitude squared Fast Fourier Transform of the signals that were illustrated in", "type": "text" } ], "index": 14 }, { "bbox": [ 105, 511, 505, 524 ], "spans": [ { "bbox": [ 105, 511, 505, 524 ], "score": 1.0, "content": "Figure 2. The attack designed with the derivative loss term has a similar distribution as the original", "type": "text" } ], "index": 15 }, { "bbox": [ 105, 523, 505, 535 ], "spans": [ { "bbox": [ 105, 523, 505, 535 ], "score": 1.0, "content": "signal, where as the attack without derivative shows large contributions in the low frequency domain", "type": "text" } ], "index": 16 }, { "bbox": [ 107, 533, 505, 546 ], "spans": [ { "bbox": [ 107, 533, 139, 545 ], "score": 0.85, "content": "( < 5 \\mathrm { H z } )", "type": "inline_equation" }, { "bbox": [ 139, 533, 505, 546 ], "score": 1.0, "content": ", which were not present in the original signal. These low-frequency components stem from", "type": "text" } ], "index": 17 }, { "bbox": [ 106, 545, 506, 557 ], "spans": [ { "bbox": [ 106, 545, 506, 557 ], "score": 1.0, "content": "the square-wave shaped attack and can be used as a way to detect the attack; hence, this attack cannot", "type": "text" } ], "index": 18 }, { "bbox": [ 106, 556, 224, 568 ], "spans": [ { "bbox": [ 106, 556, 224, 568 ], "score": 1.0, "content": "be considered imperceptible.", "type": "text" } ], "index": 19 } ], "index": 15.5, "bbox_fs": [ 105, 478, 506, 568 ] }, { "type": "text", "bbox": [ 106, 572, 505, 671 ], "lines": [ { "bbox": [ 106, 572, 505, 584 ], "spans": [ { "bbox": [ 106, 572, 505, 584 ], "score": 1.0, "content": "Moreover, Figure 13 shows the attacks with and without derivative loss term with increasing maximum", "type": "text" } ], "index": 20 }, { "bbox": [ 105, 582, 505, 595 ], "spans": [ { "bbox": [ 105, 582, 309, 595 ], "score": 1.0, "content": "amplitude \u000f. We can see that for low amplitudes (", "type": "text" }, { "bbox": [ 309, 583, 333, 594 ], "score": 0.56, "content": "\\mathrm { 1 m V }", "type": "inline_equation" }, { "bbox": [ 333, 582, 351, 595 ], "score": 1.0, "content": "and", "type": "text" }, { "bbox": [ 351, 583, 375, 594 ], "score": 0.47, "content": "5 \\mathrm { m V }", "type": "inline_equation" }, { "bbox": [ 375, 582, 505, 595 ], "score": 1.0, "content": ") the generated attacks with and", "type": "text" } ], "index": 21 }, { "bbox": [ 105, 593, 505, 607 ], "spans": [ { "bbox": [ 105, 593, 280, 607 ], "score": 1.0, "content": "without derivative still look like EEGs. At", "type": "text" }, { "bbox": [ 280, 594, 307, 605 ], "score": 0.7, "content": "1 0 \\mathrm { m V } ,", "type": "inline_equation" }, { "bbox": [ 308, 593, 505, 607 ], "score": 1.0, "content": "the attack generated without derivative presents", "type": "text" } ], "index": 22 }, { "bbox": [ 105, 604, 506, 618 ], "spans": [ { "bbox": [ 105, 604, 457, 618 ], "score": 1.0, "content": "minor square-wave artifacts, which could be still imperceptible to a non-expert. With", "type": "text" }, { "bbox": [ 458, 605, 487, 615 ], "score": 0.73, "content": "2 5 \\mathrm { m V }", "type": "inline_equation" }, { "bbox": [ 487, 604, 506, 618 ], "score": 1.0, "content": "and", "type": "text" } ], "index": 23 }, { "bbox": [ 106, 615, 506, 629 ], "spans": [ { "bbox": [ 106, 616, 134, 627 ], "score": 0.7, "content": "5 0 \\mathrm { m V } ,", "type": "inline_equation" }, { "bbox": [ 134, 615, 506, 629 ], "score": 1.0, "content": ", the ones generated without derivative have strong and perceptible square-wave displacements,", "type": "text" } ], "index": 24 }, { "bbox": [ 106, 627, 505, 639 ], "spans": [ { "bbox": [ 106, 627, 505, 639 ], "score": 1.0, "content": "while the ones generated with our proposed method can still be mistaken as real EEG signals. While", "type": "text" } ], "index": 25 }, { "bbox": [ 106, 638, 504, 650 ], "spans": [ { "bbox": [ 106, 638, 382, 650 ], "score": 1.0, "content": "with the instance-based attacks, it is not necessary to have more than", "type": "text" }, { "bbox": [ 382, 638, 410, 648 ], "score": 0.75, "content": "1 0 \\mathrm { m V }", "type": "inline_equation" }, { "bbox": [ 410, 638, 504, 650 ], "score": 1.0, "content": "to get a very high ASR", "type": "text" } ], "index": 26 }, { "bbox": [ 104, 647, 506, 663 ], "spans": [ { "bbox": [ 104, 647, 506, 663 ], "score": 1.0, "content": "(see Figure 1), with the universal attacks and physical constraints, the ASR increases with increasing", "type": "text" } ], "index": 27 }, { "bbox": [ 105, 660, 260, 673 ], "spans": [ { "bbox": [ 105, 660, 260, 673 ], "score": 1.0, "content": "perturbation amplitude (see Figure 4).", "type": "text" } ], "index": 28 } ], "index": 24, "bbox_fs": [ 104, 572, 506, 673 ] }, { "type": "text", "bbox": [ 107, 677, 505, 732 ], "lines": [ { "bbox": [ 106, 677, 505, 689 ], "spans": [ { "bbox": [ 106, 677, 505, 689 ], "score": 1.0, "content": "The same observations can be drawn from the plausibility metrics, which have been proposed for the", "type": "text" } ], "index": 29 }, { "bbox": [ 106, 688, 505, 700 ], "spans": [ { "bbox": [ 106, 688, 505, 700 ], "score": 1.0, "content": "first time in this paper to assess quantitatively the EEG attacks. For example, looking at the cosine", "type": "text" } ], "index": 30 }, { "bbox": [ 105, 697, 507, 713 ], "spans": [ { "bbox": [ 105, 697, 147, 713 ], "score": 1.0, "content": "similarity", "type": "text" }, { "bbox": [ 147, 699, 160, 710 ], "score": 0.8, "content": "( \\gamma )", "type": "inline_equation" }, { "bbox": [ 161, 697, 335, 713 ], "score": 1.0, "content": "in Table 1, without the derivative loss term,", "type": "text" }, { "bbox": [ 335, 700, 343, 710 ], "score": 0.8, "content": "\\gamma", "type": "inline_equation" }, { "bbox": [ 343, 697, 378, 713 ], "score": 1.0, "content": "drops to", "type": "text" }, { "bbox": [ 378, 699, 410, 709 ], "score": 0.88, "content": "9 7 . 9 9 \\%", "type": "inline_equation" }, { "bbox": [ 411, 697, 431, 713 ], "score": 1.0, "content": "with", "type": "text" }, { "bbox": [ 432, 699, 466, 709 ], "score": 0.89, "content": "\\epsilon = 5 \\mathrm { m V } ,", "type": "inline_equation" }, { "bbox": [ 466, 697, 507, 713 ], "score": 1.0, "content": 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Fig. 11 shows the confusion", "type": "text" } ], "index": 36 }, { "bbox": [ 106, 596, 504, 607 ], "spans": [ { "bbox": [ 106, 596, 504, 607 ], "score": 1.0, "content": "matrix of EEGNet on the Physionet dataset before the attack, where all classes can be classified with", "type": "text" } ], "index": 37 }, { "bbox": [ 106, 607, 505, 619 ], "spans": [ { "bbox": [ 106, 607, 174, 619 ], "score": 1.0, "content": "similar accuracy", "type": "text" }, { "bbox": [ 174, 607, 234, 617 ], "score": 0.88, "content": "( 7 2 . 8 \\% - 7 3 . 5 \\% )", "type": "inline_equation" }, { "bbox": [ 235, 607, 505, 619 ], "score": 1.0, "content": ". Fig. 12 shows the confusion matrices for three different propagation", "type": "text" } ], "index": 38 }, { "bbox": [ 104, 618, 506, 630 ], "spans": [ { "bbox": [ 104, 618, 153, 630 ], "score": 1.0, "content": "parameters", "type": "text" }, { "bbox": [ 154, 618, 223, 630 ], "score": 0.9, "content": "( \\lambda _ { m } \\in \\{ 1 , 5 , 1 5 \\} )", "type": "inline_equation" }, { "bbox": [ 223, 618, 506, 630 ], "score": 1.0, "content": "and two attack positions (T9 and T10) which correspond to the left and", "type": "text" } ], "index": 39 }, { "bbox": [ 105, 628, 506, 641 ], "spans": [ { "bbox": [ 105, 628, 506, 641 ], "score": 1.0, "content": "right side of the head. When considering the attacks from the left side, shown in Fig. 12a–12c, more", "type": "text" } ], "index": 40 }, { "bbox": [ 106, 640, 505, 652 ], "spans": [ { "bbox": [ 106, 640, 505, 652 ], "score": 1.0, "content": "samples with ground-truth label “right” can be fooled to “rest”. This is particularly articulated in", "type": "text" } ], "index": 41 }, { "bbox": [ 105, 651, 505, 663 ], "spans": [ { "bbox": [ 105, 651, 259, 663 ], "score": 1.0, "content": "largely attenuated propagation model", "type": "text" }, { "bbox": [ 259, 651, 292, 662 ], "score": 0.86, "content": "\\lambda _ { m } { = } 1 5 )", "type": "inline_equation" }, { "bbox": [ 292, 651, 505, 663 ], "score": 1.0, "content": ". 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Fig. 11 shows the confusion", "type": "text" } ], "index": 36 }, { "bbox": [ 106, 596, 504, 607 ], "spans": [ { "bbox": [ 106, 596, 504, 607 ], "score": 1.0, "content": "matrix of EEGNet on the Physionet dataset before the attack, where all classes can be classified with", "type": "text" } ], "index": 37 }, { "bbox": [ 106, 607, 505, 619 ], "spans": [ { "bbox": [ 106, 607, 174, 619 ], "score": 1.0, "content": "similar accuracy", "type": "text" }, { "bbox": [ 174, 607, 234, 617 ], "score": 0.88, "content": "( 7 2 . 8 \\% - 7 3 . 5 \\% )", "type": "inline_equation" }, { "bbox": [ 235, 607, 505, 619 ], "score": 1.0, "content": ". Fig. 12 shows the confusion matrices for three different propagation", "type": "text" } ], "index": 38 }, { "bbox": [ 104, 618, 506, 630 ], "spans": [ { "bbox": [ 104, 618, 153, 630 ], "score": 1.0, "content": "parameters", "type": "text" }, { "bbox": [ 154, 618, 223, 630 ], "score": 0.9, "content": "( \\lambda _ { m } \\in \\{ 1 , 5 , 1 5 \\} )", "type": "inline_equation" }, { "bbox": [ 223, 618, 506, 630 ], "score": 1.0, "content": "and two attack positions (T9 and T10) which correspond to the left and", "type": "text" } ], "index": 39 }, { "bbox": [ 105, 628, 506, 641 ], "spans": [ { "bbox": [ 105, 628, 506, 641 ], "score": 1.0, "content": "right side of the head. When considering the attacks from the left side, shown in Fig. 12a–12c, more", "type": "text" } ], "index": 40 }, { "bbox": [ 106, 640, 505, 652 ], "spans": [ { "bbox": [ 106, 640, 505, 652 ], "score": 1.0, "content": "samples with ground-truth label “right” can be fooled to “rest”. 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