SafePaint: Anti-forensic Image Inpainting with Domain Adaptation

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 inpainted region and the unaltered area to achieve anti-forensic goals. Through comprehensive theoretical analysis, we validate the effectiveness of domain adaptation for anti-forensic performance. Furthermore, we meticulously crafted a region-wise separated attention (RWSA) module, which not only aligns with our objective of anti-forensics but also enhances the performance of the model. Extensive qualitative and quantitative evaluations show our approach achieves comparable results to existing image inpainting methods while offering anti-forensic capabilities not available in other methods.