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.

提出了一个用于反取证图像修复的端到端训练框架，名为 SafePaint，用于解决生成逼真图像内容时常出现痕迹的问题，并通过领域自适应方法实现反取证的目标