1. Auto-focus tracing: Image manipulation detection with artifact graph contrastive.
- Author
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Pan, Wenyan, Xia, Zhihua, Ma, Wentao, Wang, Yuwei, Gu, Lichuan, Shi, Guolong, and Zhao, Shan
- Subjects
- *
DIGITAL forensics , *PIXELS - Abstract
Existing image manipulation detection methods rely on manipulation traces, leading to sub-optimal performance. This is primarily due to their challenges in distinguishing manipulation traces amidst extensive noisy information in the entire image and overlooking artifact relations between manipulated and authentic regions. Toward this end, we propose an Auto-Focus Graph Contrastive Learning, called AFGCL, for image manipulation detection, incorporating two innovative modules: multi-scale view generation (MSVG) and artifact relations modeling (ARM). The MSVG module focuses on generating a pair of contrastive views, each encompassing a different scale region of interest, namely the manipulated region and its surrounding authentic region. Meanwhile, the ARM module emphasizes modeling artifact relations between the pixels of the region of interest to learn a discriminative representation. By minimizing the distance between representations and artifact relations of corresponding views, the AFGCL automatically focuses on the region of interest. This enables a thorough exploration of artifact relations, facilitating accurate manipulation detection. The results show that AFGCL outperforms previous state-of-the-art methods on widely used benchmarks. Code is available at: https://github.com/pwy-cmd/AFGCL. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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