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+ "content": "Ziye Wang Minghang Yu Chunyan Xu* Zhen Cui Nanjing University of Science and Technology, Nanjing, China"
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+ "content": "With the rapid advancement of image generation techniques, robust forgery detection has become increasingly imperative to ensure the trustworthiness of digital media. Recent research indicates that the learned semantic concepts of pre-trained models are critical for identifying fake images. However, the misalignment between the forgery and semantic concept spaces hinders the model's forgery detection performance. To address this problem, we propose a novel Semantic Discrepancy-aware Detector (SDD) that leverages reconstruction learning to align the two spaces at a fine-grained visual level. By exploiting the conceptual knowledge embedded in the pre-trained vision-language model, we specifically design a semantic token sampling module to mitigate the space shifts caused by features irrelevant to both forgery traces and semantic concepts. A concept-level forgery discrepancy learning module, based on reconstruction, enhances the interaction between semantic concepts and forgery traces, effectively capturing discrepancies under the concepts' guidance. Finally, the low-level forgery feature enhancement integrates the learned concept-level forgery discrepancies to minimize redundant forgery information. Experiments conducted on two standard image forgery datasets demonstrate the efficacy of the proposed SDD, which achieves superior results compared to existing methods. The code is available at https://github.com/wzy111111/SSD."
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+ "content": "With the thriving of generative AI technologies, like Generative Adversarial Networks (GANs) [14] and diffusion models [2], the images generated by these models can easily create confusion by passing off the spurious as genuine. Therefore, it is crucial to develop a universal method for detecting fake images to mitigate the widespread dissemination of disinformation."
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+ "content": " can reflect the similarity of image descriptions, we model the feature space in polar coordinates. As the semantic concept space in [32] is frozen, fake samples sharing similar concepts with real ones can be easily misclassified. With forgery-adaptive space like [26], the model can correctly distinguish between them based on re-learned forgery features. Nevertheless, due to the semantic concept bias introduced by coarse text prompts, the target samples may be projected into an inaccurate semantic concept dimension, causing them to drift away from the real source samples along the fake dimension."
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+ "content": "sitioning to the CLIP's space, these differences become inconsistent. From this, we infer a nuanced relationship between semantic concepts and forgery traces: Different semantic concepts may guide the model to uncover distinct forgery traces."
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+ "content": "We develop a vision-based paradigm, as outlined in Fig. 2. First, employing a pre-trained model only with nearest neighbor or linear probing (e.g. UnivFD [32], Fig. 2 (a)) is suboptimal for image forgery detection. Second, modifying the pre-trained model with task-specific prompts (e.g. FatFormer [26], Fig. 2 (b)) may favor models biased towards any particular semantic concept. These studies pave the way for exploring pre-trained space with rich semantic concepts. Inspired by image reconstruction [43, 53], our paradigm amplifies the concept-level forgery discrepancies of forgery images, which empowers the model to detect sus"
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+ "content": "the common forgery traces between all real and fake images, such as noise patterns, texture statistics, and frequency signals. As an illustration, Liu et al. [24] designed a network that learns the consistent noise patterns in images for fake detection. Liu et al. [28] proposed to leverage the gram matrix to discover the global anomalous texture of fake images. An effective approach [13] demonstrated that frequency representation is an important factor in improving fake detection performance. However, these differences are rigorously specific to the monotonous features, which contribute to the issue of overfitting. Cutting-edge research [26, 32] shifted attention toward the semantic properties of images. Ojha et al. [32] showed that projecting images into the feature space of pre-trained vision-language model enables strong generalization ability. To build generalized forgery representations, Liu et al. [26] constructed forgery adaptive space by a forgery-aware adapter. The above research [5, 26, 32] has suggested that concept attributes are vital in the image forgery detection task. Assuming that diffusion-based models leave distinct forgery traces that are characteristic of specific concept distributions, we aim to extract robust forgery features guided by semantic concepts, rather than suppressing them. Therefore, even \"useless\" information can be useful by providing significant certainty about the content of the image."
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+ "content": "great potential in unsupervised representation learning [16, 27]. Some works [5, 39] utilized reconstruction learning to reveal the nuances between real and fake images. For example, Wang et al. [51] found that reconstructing images by DDIM exposes an error between real images and their reconstructed replica. The new synthetic image detection method[5] used text-conditioned inversion maps to learn internal representations, which is conducive to predicting whether an image is fake. Ricker et al. [39] offered a simple detection approach by applying AE to measure the reconstruction error. Notably, these works are committed to reconstructing the distributions of both real and fake samples by leveraging generative models. Unlike previous works, we focus solely on reconstructing real images in the finetuned CLIP space in light of the authenticity and richness of semantic concepts. The distribution of real samples, learned from pre-trained vision-language model, helps to define an optimal boundary, thus alleviating overfitting."
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+ "html": "| Methods | Ref | GAN | Deep fakes | Low level | Perceptual loss | Guided | LDM | Glide | Dalle | mAP | |
| Pro-GAN | Cycle-GAN | Big-GAN | Style-GAN | Gau-GAN | Star-GAN | SITD | SAN | CRN | IMLE | 200Steps | 200w/cfg | 100Steps | 100w/ CFG | 10027 | 5027 | 10 | | |
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| PatchFor | ECCV2020 | 80.88 | 72.84 | 71.66 | 85.75 | 65.99 | 69.25 | 76.55 | 76.19 | 76.34 | 74.52 | 68.52 | 75.03 | 87.10 | 86.72 | 86.40 | 85.37 | 83.73 | 78.38 | 75.67 | 77.73 | |
| Co-occurrence | Elect.Imag. | 99.74 | 80.95 | 50.61 | 98.63 | 53.11 | 67.99 | 59.14 | 68.98 | 60.42 | 73.06 | 87.21 | 70.20 | 91.21 | 89.02 | 92.39 | 89.32 | 88.35 | 82.79 | 80.96 | 78.11 | |
| Freq-spec | WIFS2019 | 55.39 | 100.0 | 75.08 | 55.11 | 66.08 | 100.0 | 45.18 | 47.46 | 57.12 | 53.61 | 50.98 | 57.72 | 77.72 | 77.25 | 76.47 | 68.58 | 64.58 | 61.92 | 67.77 | 66.21 | |
| Dire | ICCV2023 | 100.0 | 83.59 | 81.50 | 96.50 | 81.70 | 99.88 | 95.73 | 62.51 | 69.98 | 97.31 | 98.62 | 79.53 | 75.52 | 73.42 | 76.45 | 86.28 | 89.00 | 88.34 | 51.35 | 83.54 | |
| UnivFD | CVPR2023 | 100.0 | 99.46 | 99.59 | 97.24 | 99.98 | 99.60 | 82.45 | 61.32 | 79.02 | 96.72 | 99.00 | 87.77 | 99.14 | 92.15 | 99.17 | 94.74 | 95.34 | 94.57 | 97.15 | 93.38 | |
| NPR | CVPR2024 | 100.0 | 99.50 | 96.50 | 99.80 | 96.80 | 100.0 | 92.20 | 73.10 | 78.70 | 87.20 | 64.80 | 65.80 | 99.80 | 99.80 | 99.80 | 99.70 | 99.80 | 99.80 | 98.60 | 92.19 | |
| FatFormer | CVPR2024 | 100.0 | 100.0 | 99.98 | 99.75 | 100.0 | 100.0 | 97.99 | 97.94 | 81.23 | 99.84 | 99.93 | 91.92 | 99.83 | 99.22 | 99.89 | 99.27 | 99.50 | 99.33 | 99.84 | 98.18 | |
| Ours | | 100.0 | 99.77 | 99.93 | 99.48 | 99.98 | 99.97 | 97.23 | 97.91 | 93.10 | 99.79 | 99.96 | 92.06 | 99.88 | 98.95 | 99.92 | 98.06 | 98.29 | 97.73 | 99.81 | 98.51 | |
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| Pro-GAN | Cycle-GAN | Big-GAN | Style-GAN | Gau-GAN | Star-GAN | SITD | SAN | CRN | IMLE | 200 Steps | 200 wcfg | 100 Steps | 100 27 | 50 27 | 100 10 | | |
| CNN-Spot | CVPR2020 | 99.99 | 85.20 | 70.20 | 85.70 | 78.95 | 91.70 | 53.47 | 66.67 | 48.69 | 86.31 | 86.26 | 60.07 | 54.03 | 54.96 | 54.14 | 60.78 | 63.80 | 65.66 | 55.58 | 69.58 |
| PatchFor | ECCV2020 | 75.03 | 68.97 | 68.47 | 79.16 | 64.23 | 63.94 | 75.54 | 75.14 | 75.28 | 72.33 | 55.30 | 67.41 | 76.50 | 76.10 | 75.77 | 74.81 | 73.28 | 68.52 | 67.91 | 71.24 |
| Co-occurrence | Elect.Imag. | 97.70 | 63.15 | 53.75 | 92.50 | 51.10 | 54.70 | 57.10 | 63.06 | 55.85 | 65.65 | 65.80 | 60.50 | 70.70 | 70.55 | 71.00 | 70.25 | 69.60 | 69.90 | 67.55 | 66.86 |
| Freq-spec | WIFS2019 | 49.90 | 99.90 | 50.50 | 49.90 | 50.30 | 99.70 | 50.10 | 50.00 | 48.00 | 50.60 | 50.10 | 50.90 | 50.40 | 50.40 | 50.30 | 51.70 | 51.40 | 50.40 | 50.00 | 55.45 |
| Dire | ICCV2023 | 99.86 | 73.47 | 60.68 | 72.39 | 65.15 | 93.60 | 88.86 | 52.78 | 56.39 | 90.07 | 94.05 | 61.05 | 59.35 | 59.95 | 60.65 | 69.30 | 72,70 | 71.00 | 52.75 | 71.19 |
| UnivFD | CVPR2023 | 100.0 | 98.50 | 94.50 | 82.00 | 99.50 | 97.00 | 66.60 | 63.00 | 57.50 | 59.50 | 72.00 | 70.03 | 94.19 | 73.76 | 94.36 | 79.07 | 79.85 | 78.14 | 86.78 | 81.38 |
| NPR | CVPR2024 | 99.80 | 92.00 | 89.50 | 96.30 | 87.60 | 99.70 | 79.40 | 61.40 | 70.60 | 74.50 | 57.10 | 55.23 | 97.40 | 98.70 | 97.90 | 97.00 | 97.90 | 97.00 | 88.80 | 86.20 |
| FatFormer | CVPR2024 | 99.89 | 99.36 | 99.50 | 97.12 | 99.43 | 99.75 | 93.25 | 81.39 | 68.04 | 69.47 | 69.47 | 76.00 | 98.55 | 94.85 | 98.60 | 94.30 | 94.60 | 94.15 | 98.70 | 90.86 |
| Ours | | 99.88 | 95.76 | 96.70 | 98.08 | 98.46 | 99.17 | 91.82 | 83.61 | 77.45 | 95.40 | 96.47 | 79.55 | 98.05 | 94.60 | 98.25 | 92.20 | 93.35 | 91.80 | 98.00 | 93.61 |
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+ "html": "| Methods | CNN-Spot | Freq-spec | Dire | Univ FD | NPR | FatF ormer | FakeIn version | Patch For | Ours |
| Kandinsky2 | 60.0 | 57.0 | 71.6 | 56.2 | 97.5 | 75.6 | 69.9 | 53.5 | 97.13 |
| Kandinsky3 | 65.9 | 45.7 | 74.9 | 61.4 | 93.7 | 80.1 | 74.3 | 51.4 | 94.8 |
| PixArt-α | 62.7 | 56.4 | 81.5 | 64.7 | 89.5 | 75.3 | 73.0 | 49.6 | 90.2 |
| SDXL-DPO | 84.3 | 69.8 | 69.9 | 70.2 | 97.6 | 86.0 | 88.1 | 54.5 | 95.5 |
| Segmind-Vega | 74.2 | 65.3 | 81.0 | 62.3 | 97.1 | 82.4 | 81.1 | 53.1 | 97.0 |
| SDXL | 81.4 | 61.2 | 86.2 | 66.3 | 96.0 | 85.1 | 80.7 | 63.9 | 97.6 |
| Seg-MoE | 66.3 | 54.6 | 71.9 | 62.0 | 93.6 | 70.8 | 71.3 | 49.8 | 97.6 |
| SSD-1B | 72.6 | 67.8 | 79.8 | 62.8 | 99.6 | 70.1 | 79.4 | 61.2 | 99.8 |
| Stable-Cascade | 70.5 | 62.1 | 74.1 | 68.2 | 97.6 | 81.6 | 74.9 | 57.4 | 99.2 |
| Würstchen2 | 61.0 | 63.3 | 74.2 | 69.7 | 90.9 | 72.9 | 70.5 | 47.2 | 98.2 |
| Midjourney | 63.0 | 50.9 | 72.4 | 59.2 | 58.5 | 73.6 | 66.4 | 53.7 | 90.0 |
| Playground | 58.2 | 52.3 | 67.9 | 58.7 | 93.1 | 81.4 | 62.5 | 54.1 | 92.8 |
| DALL-E3 | 71.6 | 59.9 | 80.8 | 48.0 | 69.1 | 79.2 | 75.9 | 50.1 | 85.9 |
| Average | 68.6 | 58.9 | 75.9 | 62.3 | 90.3 | 78.0 | 74.5 | 53.8 | 95.1 |
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| 1 | | ✓ | | 97.37 | 81.64 |
| 2 | | ✓ | ✓ | 97.41 | 90.17 |
| 3 | ✓ | ✓ | | 97.39 | 89.98 |
| 4 | ✓ | ✓ | ✓ | 98.52 | 93.61 |
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