Image Manipulation Detection On Dso 1
Metrics
AUC
Balanced Accuracy
Results
Performance results of various models on this benchmark
Model Name | AUC | Balanced Accuracy | Paper Title | Repository |
|---|---|---|---|---|
| TruFor | .984 | .930 | TruFor: Leveraging all-round clues for trustworthy image forgery detection and localization | - |
| ManTraNet | .874 | .500 | ManTra-Net: Manipulation Tracing Network for Detection and Localization of Image Forgeries With Anomalous Features | |
| Late Fusion | .958 | .830 | MMFusion: Combining Image Forensic Filters for Visual Manipulation Detection and Localization | - |
| CAT-Net v2 | .747 | .525 | Learning JPEG Compression Artifacts for Image Manipulation Detection and Localization | - |
| SPAN | .669 | .233 | SPAN: Spatial Pyramid Attention Network for Image Manipulation Localization | |
| CR-CNN | .576 | .289 | Constrained R-CNN: A general image manipulation detection model | - |
| Early Fusion | .966 | .935 | MMFusion: Combining Image Forensic Filters for Visual Manipulation Detection and Localization | - |
| MVSS-Net | .552 | .358 | Image Manipulation Detection by Multi-View Multi-Scale Supervision | - |
| DF-Net | 0.769 | - | DF-Net: The Digital Forensics Network for Image Forgery Detection |
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