SafePaint: Anti-forensic Image Inpainting with Domain Adaptation
Dunyun Chen, Xin Liao, Xiaoshuai Wu, Shiwei Chen

TL;DR
SafePaint introduces an anti-forensic image inpainting framework that not only restores missing image regions but also conceals tampering traces through domain adaptation and a novel attention module, enhancing security against forensic detection.
Contribution
The paper proposes a novel end-to-end training framework for anti-forensic image inpainting, integrating domain adaptation and a region-wise attention module to conceal tampering traces effectively.
Findings
Achieves comparable inpainting quality to existing methods.
Provides effective anti-forensic capabilities to hide tampering traces.
Validated through extensive qualitative and quantitative evaluations.
Abstract
Existing image inpainting methods have achieved remarkable accomplishments in generating visually appealing results, often accompanied by a trend toward creating more intricate structural textures. However, while these models excel at creating more realistic image content, they often leave noticeable traces of tampering, posing a significant threat to security. In this work, we take the anti-forensic capabilities into consideration, firstly proposing an end-to-end training framework for anti-forensic image inpainting named SafePaint. Specifically, we innovatively formulated image inpainting as two major tasks: semantically plausible content completion and region-wise optimization. The former is similar to current inpainting methods that aim to restore the missing regions of corrupted images. The latter, through domain adaptation, endeavors to reconcile the discrepancies between the…
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Taxonomy
TopicsDigital Media Forensic Detection · Generative Adversarial Networks and Image Synthesis · AI in cancer detection
MethodsInpainting
