Papers › Learning JPEG Compression Artifacts for Image Manipulation Detection and Localization
Learning JPEG Compression Artifacts for Image Manipulation Detection and Localization
Myung-Joon Kwon, Seung-Hun Nam, In-Jae Yu, Heung-Kyu Lee, Changick Kim
Detecting and localizing image manipulation are necessary to counter malicious use of image editing techniques. Accordingly, it is essential to distinguish between authentic and tampered regions by analyzing intrinsic statistics in an image. We focus on JPEG compression artifacts left during image acquisition and editing. We propose a convolutional neural network (CNN) that uses discrete cosine transform (DCT) coefficients, where compression artifacts remain, to localize image manipulation. Standard CNNs cannot learn the distribution of DCT coefficients because the convolution throws away the spatial coordinates, which are essential for DCT coefficients. We illustrate how to design and train a neural network that can learn the distribution of DCT coefficients. Furthermore, we introduce Compression Artifact Tracing Network (CAT-Net) that jointly uses image acquisition artifacts and compression artifacts. It significantly outperforms traditional and deep neural network-based methods in detecting and localizing tampered regions.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Manipulation Detection | COVERAGE | CAT-Net v2 | AUC | .680 | #4 of 8 | Archive leaderboard | report |
| Image Manipulation Detection | COVERAGE | CAT-Net v2 | Balanced Accuracy | .635 | #4 of 8 | Archive leaderboard | report |
| Image Manipulation Detection | Casia V1+ | CAT-Net v2 | AUC | .942 | #3 of 9 | Archive leaderboard | report |
| Image Manipulation Detection | Casia V1+ | CAT-Net v2 | Balanced Accuracy | .838 | #3 of 9 | Archive leaderboard | report |
| Image Manipulation Detection | CocoGlide | CAT-Net v2 | AUC | .667 | #4 of 8 | Archive leaderboard | report |
| Image Manipulation Detection | CocoGlide | CAT-Net v2 | Balanced Accuracy | .580 | #4 of 8 | Archive leaderboard | report |
| Image Manipulation Detection | Columbia | CAT-Net v2 | AUC | .977 | #4 of 8 | Archive leaderboard | report |
| Image Manipulation Detection | Columbia | CAT-Net v2 | Balanced Accuracy | .803 | #4 of 8 | Archive leaderboard | report |
| Image Manipulation Detection | DSO-1 | CAT-Net v2 | AUC | .747 | #4 of 9 | Archive leaderboard | report |
| Image Manipulation Detection | DSO-1 | CAT-Net v2 | Balanced Accuracy | .525 | #4 of 9 | Archive leaderboard | report |
| Image Manipulation Localization | CASIAv1(Protoclo-CAT) | CAT-Net | Pixel Binary F1 | 0.808 | #3 of 8 | Archive leaderboard | report |
| Image Manipulation Localization | COVERAGE | CAT-Net v2 | Average Pixel F1(Fixed threshold) | .381 | #9 of 11 | Archive leaderboard | report |
| Image Manipulation Localization | COVERAGE(Protocol-CAT) | CAT-Net | Pixel Binary F1 | 0.427 | #5 of 8 | Archive leaderboard | report |
| Image Manipulation Localization | Casia V1+ | CAT-Net v2 | Average Pixel F1(Fixed threshold) | .752 | #6 of 11 | Archive leaderboard | report |
| Image Manipulation Localization | CocoGlide | CAT-Net v2 | Average Pixel F1(Fixed threshold) | .434 | #10 of 11 | Archive leaderboard | report |
| Image Manipulation Localization | Columbia | CAT-Net v2 | Average Pixel F1(Fixed threshold) | .859 | #6 of 11 | Archive leaderboard | report |
| Image Manipulation Localization | Columbia(Protocol-CAT) | CAT-Net | Pixel Binary F1 | 0.915 | #3 of 8 | Archive leaderboard | report |
| Image Manipulation Localization | DSO-1 | CAT-Net v2 | Average Pixel F1(Fixed threshold) | .584 | #7 of 11 | Archive leaderboard | report |
| Image Manipulation Localization | NIST16(Protocol-CAT) | CAT-Net | Pixel Binary F1 | 0.252 | #6 of 8 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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