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In this work, we present the first study that analyzes", + "type": "text" + } + ], + "index": 13 + }, + { + "bbox": [ + 142, + 309, + 470, + 321 + ], + "spans": [ + { + "bbox": [ + 142, + 309, + 470, + 321 + ], + "score": 1.0, + "content": "and models adversarial attacks based on physical domain constraints in EEG-", + "type": "text" + } + ], + "index": 14 + }, + { + "bbox": [ + 141, + 320, + 469, + 332 + ], + "spans": [ + { + "bbox": [ + 141, + 320, + 469, + 332 + ], + "score": 1.0, + "content": "based BCIs. Specifically, we assess the robustness of EEGNet which is the", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 141, + 331, + 469, + 343 + ], + "spans": [ + { + "bbox": [ + 141, + 331, + 469, + 343 + ], + "score": 1.0, + "content": "current state-of-the-art network for embedded BCIs. We propose new methods", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 141, + 342, + 469, + 354 + ], + "spans": [ + { + "bbox": [ + 141, + 342, + 469, + 354 + ], + "score": 1.0, + "content": "to induce denial-of-service attacks and incorporate domain-specific insights and", + "type": "text" + } + ], + "index": 17 + }, + { + "bbox": [ + 141, + 353, + 470, + 365 + ], + "spans": [ + { + "bbox": [ + 141, + 353, + 470, + 365 + ], + "score": 1.0, + "content": "constraints to accomplish two key goals: (i) create smooth adversarial attacks", + "type": "text" + } + ], + "index": 18 + }, + { + "bbox": [ + 141, + 364, + 470, + 375 + ], + "spans": [ + { + "bbox": [ + 141, + 364, + 470, + 375 + ], + "score": 1.0, + "content": "that are physiologically plausible; (ii) consider the realistic case where the attack", + "type": "text" + } + ], + "index": 19 + }, + { + "bbox": [ + 141, + 375, + 470, + 387 + ], + "spans": [ + { + "bbox": [ + 141, + 375, + 470, + 387 + ], + "score": 1.0, + "content": "happens at the origin of the signal acquisition and it propagates on the human head.", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 141, + 385, + 470, + 398 + ], + "spans": [ + { + "bbox": [ + 141, + 385, + 470, + 398 + ], + "score": 1.0, + "content": "Our results show that EEGNet is significantly vulnerable to adversarial attacks", + "type": "text" + } + ], + "index": 21 + }, + { + "bbox": [ + 141, + 396, + 469, + 408 + ], + "spans": [ + { + "bbox": [ + 141, + 396, + 308, + 408 + ], + "score": 1.0, + "content": "with an attack success rate of more than", + "type": "text" + }, + { + "bbox": [ + 309, + 397, + 329, + 407 + ], + "score": 0.86, + "content": "50 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 329, + 396, + 469, + 408 + ], + "score": 1.0, + "content": ". With our work, we want to raise", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 141, + 407, + 443, + 420 + ], + "spans": [ + { + "bbox": [ + 141, + 407, + 443, + 420 + ], + "score": 1.0, + "content": "awareness and incentivize future developments of proper countermeasures.", + "type": "text" + } + ], + "index": 23 + } + ], + "index": 14, + "bbox_fs": [ + 141, + 210, + 470, + 420 + ] + }, + { + "type": "title", + "bbox": [ + 108, + 438, + 206, + 450 + ], + "lines": [ + { + "bbox": [ + 105, + 437, + 208, + 453 + ], + "spans": [ + { + "bbox": [ + 105, + 437, + 208, + 453 + ], + "score": 1.0, + "content": "1 INTRODUCTION", + "type": "text" + } + ], + "index": 24 + } + ], + "index": 24 + }, + { + "type": "text", + "bbox": [ + 107, + 463, + 505, + 616 + ], + "lines": [ + { + "bbox": [ + 105, + 461, + 505, + 476 + ], + "spans": [ + { + "bbox": [ + 105, + 461, + 505, + 476 + ], + "score": 1.0, + "content": "Recent work has shown that adversarial perturbations can cause state-of-the-art (SoA) deep learning", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 106, + 474, + 506, + 485 + ], + "spans": [ + { + "bbox": [ + 106, + 474, + 506, + 485 + ], + "score": 1.0, + "content": "models to misbehave in various domains including vision (Szegedy et al., 2014; Goodfellow et al.,", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 106, + 484, + 506, + 497 + ], + "spans": [ + { + "bbox": [ + 106, + 484, + 506, + 497 + ], + "score": 1.0, + "content": "2015), NLP (Li et al., 2019a; Zhang et al., 2020), speech (Qin et al., 2019; Li et al., 2019b), and", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 106, + 495, + 505, + 508 + ], + "spans": [ + { + "bbox": [ + 106, + 495, + 505, + 508 + ], + "score": 1.0, + "content": "biomedicine (Finlayson et al., 2019; Han et al., 2020). Neural networks have been applied in", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 105, + 506, + 506, + 519 + ], + "spans": [ + { + "bbox": [ + 105, + 506, + 506, + 519 + ], + "score": 1.0, + "content": "brain–computer interfaces (BCIs) achieving impressive results (Lawhern et al., 2018; Dose et al.,", + "type": "text" + } + ], + "index": 29 + }, + { + "bbox": [ + 105, + 516, + 506, + 530 + ], + "spans": [ + { + "bbox": [ + 105, + 516, + 506, + 530 + ], + "score": 1.0, + "content": "2018). A BCI enables direct interactions with external devices based on brain activities, typically", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 105, + 528, + 505, + 541 + ], + "spans": [ + { + "bbox": [ + 105, + 528, + 505, + 541 + ], + "score": 1.0, + "content": "recorded using electroencephalographic (EEG) systems. It can provide a communication pathway", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 106, + 539, + 505, + 552 + ], + "spans": [ + { + "bbox": [ + 106, + 539, + 505, + 552 + ], + "score": 1.0, + "content": "for severely paralyzed patients or assist in rehabilitation (Chaudhary et al., 2016). Besides medical", + "type": "text" + } + ], + "index": 32 + }, + { + "bbox": [ + 105, + 550, + 505, + 563 + ], + "spans": [ + { + "bbox": [ + 105, + 550, + 505, + 563 + ], + "score": 1.0, + "content": "applications, recent developments in wearable devices have pushed BCIs towards consumer-grade", + "type": "text" + } + ], + "index": 33 + }, + { + "bbox": [ + 106, + 561, + 506, + 574 + ], + "spans": [ + { + "bbox": [ + 106, + 561, + 506, + 574 + ], + "score": 1.0, + "content": "products to improve life quality (Aricò et al., 2020), e.g., the Interaxon Muse headband for stress", + "type": "text" + } + ], + "index": 34 + }, + { + "bbox": [ + 106, + 573, + 505, + 584 + ], + "spans": [ + { + "bbox": [ + 106, + 573, + 505, + 584 + ], + "score": 1.0, + "content": "relief (Arsalan et al., 2019) or the Emotiv headset for controlling drones (Marin et al., 2020) and", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 104, + 582, + 506, + 596 + ], + "spans": [ + { + "bbox": [ + 104, + 582, + 506, + 596 + ], + "score": 1.0, + "content": "ground vehicles (Zhuang et al., 2021). 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. 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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. 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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. 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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). 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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. 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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. 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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. 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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. 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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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"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. 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, + "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. 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, + "bbox_fs": [ + 105, + 542, + 506, + 587 + ] + }, + { + "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. 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The active development of smart wearable BCIs is", + "type": "text" + } + ], + "index": 3 + }, + { + "bbox": [ + 105, + 135, + 507, + 147 + ], + "spans": [ + { + "bbox": [ + 105, + 135, + 507, + 147 + ], + "score": 1.0, + "content": "introducing a paradigm shift where the processing algorithms are embedded near the data acquisition.", + "type": "text" + } + ], + "index": 4 + }, + { + "bbox": [ + 105, + 146, + 506, + 159 + ], + "spans": [ + { + "bbox": [ + 105, + 146, + 506, + 159 + ], + "score": 1.0, + "content": "While this improves the system security to a certain extend, with this work we have shown that it is", + "type": "text" + } + ], + "index": 5 + }, + { + "bbox": [ + 105, + 156, + 506, + 169 + ], + "spans": [ + { + "bbox": [ + 105, + 156, + 506, + 169 + ], + "score": 1.0, + "content": "not the only and ultimate way to a safe and reliable BCI, since we have shown that BCI systems are", + "type": "text" + } + ], + "index": 6 + }, + { + "bbox": [ + 106, + 168, + 505, + 180 + ], + "spans": [ + { + "bbox": [ + 106, + 168, + 505, + 180 + ], + "score": 1.0, + "content": "vulnerable also to attacks at the signals’ source. We hope that our work sheds light on the fact that", + "type": "text" + } + ], + "index": 7 + }, + { + "bbox": [ + 105, + 178, + 506, + 191 + ], + "spans": [ + { + "bbox": [ + 105, + 178, + 506, + 191 + ], + "score": 1.0, + "content": "practical BCI systems are vulnerable, despite the physical constraints, and motivates the design and", + "type": "text" + } + ], + "index": 8 + }, + { + "bbox": [ + 105, + 190, + 325, + 203 + ], + "spans": [ + { + "bbox": [ + 105, + 190, + 325, + 203 + ], + "score": 1.0, + "content": "development of more reliable and robust BCI systems.", + "type": "text" + } + ], + "index": 9 + } + ], + "index": 5 + }, + { + "type": "title", + "bbox": [ + 108, + 214, + 184, + 225 + ], + "lines": [ + { + "bbox": [ + 106, + 214, + 185, + 226 + ], + "spans": [ + { + "bbox": [ + 106, + 214, + 185, + 226 + ], + "score": 1.0, + "content": "REPRODUCIBLITY", + "type": "text" + } + ], + "index": 10 + } + ], + "index": 10 + }, + { + "type": "text", + "bbox": [ + 108, + 233, + 504, + 255 + ], + "lines": [ + { + "bbox": [ + 104, + 231, + 506, + 248 + ], + "spans": [ + { + "bbox": [ + 104, + 231, + 506, + 248 + ], + "score": 1.0, + "content": "A link to a anonymous downloadable source code of this work is submitted as supplementary", + "type": "text" + } + ], + "index": 11 + }, + { + "bbox": [ + 106, + 244, + 149, + 256 + ], + "spans": [ + { + "bbox": [ + 106, + 244, + 149, + 256 + ], + "score": 1.0, + "content": "materials.", + "type": "text" + } + ], + "index": 12 + } + ], + "index": 11.5 + }, + { + "type": "title", + "bbox": [ + 108, + 272, + 175, + 285 + ], + "lines": [ + { + "bbox": [ + 106, + 272, + 176, + 286 + ], + "spans": [ + { + "bbox": [ + 106, + 272, + 176, + 286 + ], + "score": 1.0, + "content": "REFERENCES", + "type": "text" + } + ], + "index": 13 + } + ], + "index": 13 + }, + { + "type": "text", + "bbox": [ + 107, + 292, + 384, + 303 + ], + "lines": [ + { + "bbox": [ + 105, + 290, + 385, + 306 + ], + "spans": [ + { + "bbox": [ + 105, + 290, + 385, + 306 + ], + "score": 1.0, + "content": "BCI2000: A general-purpose brain-computer interface (BCI) system.", + "type": "text" + } + ], + "index": 14 + } + ], + "index": 14 + }, + { + "type": "text", + "bbox": [ + 104, + 312, + 505, + 334 + ], + "lines": [ + { + "bbox": [ + 105, + 311, + 506, + 325 + ], + "spans": [ + { + "bbox": [ + 105, + 311, + 506, + 325 + ], + "score": 1.0, + "content": "Ayten Ozge Akmandor and Niraj K. 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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", 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"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. 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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. 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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. 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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. 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