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.

翻译：现有图像修复方法在生成视觉上令人满意的结果方面取得了显著成就，同时往往伴随着创造更复杂结构纹理的趋势。然而，尽管这些模型擅长生成更逼真的图像内容，但它们常常留下明显的篡改痕迹，对安全性构成了重大威胁。在本工作中，我们首次将反取证能力纳入考量，提出了一种名为SafePaint的面向反取证图像修复的端到端训练框架。具体而言，我们创新性地将图像修复公式化为两个主要任务：语义合理的区域内容补全和区域级优化。前者类似于当前旨在恢复受损图像缺失区域的修复方法。后者通过域自适应，致力于协调修复区域与未篡改区域之间的差异，以实现反取证目标。通过全面的理论分析，我们验证了域自适应对反取证性能的有效性。此外，我们精心设计了一个区域级分离注意力（RWSA）模块，该模块不仅符合我们的反取证目标，还提升了模型性能。大量定性和定量评估表明，我们的方法在提供其他方法所不具备的反取证能力的同时，达到了与现有图像修复方法可比的结果。