SafePaint：基于域适应的反取证图像修复
SafePaint: Anti-forensic Image Inpainting with Domain Adaptation
AI总结：
本文提出 SafePaint 端到端反取证图像修复框架，通过语义内容补全、域适应区域优化及 RWSA 模块，在保持修复质量的同时消除篡改痕迹。
摘要：
现有图像修复方法在生成视觉上吸引人的结果方面取得了显著成就，并往往伴随着创建更复杂结构纹理的趋势。然而，尽管这些模型擅长创建更逼真的图像内容，但它们通常会留下明显的篡改痕迹，对安全构成重大威胁。在本研究中，我们将反取证能力纳入考虑，首次提出了一个名为 SafePaint 的用于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表述为两大任务：语义合理的内容补全和区域级优化。前者类似于当前的修复方法，旨在恢复受损图像的缺失区域。后者通过域适应，努力协调修复区域与未改变区域之间的差异，以实现反取证目标。通过全面的理论分析，我们验证了域适应对反取证性能的有效性。此外，我们精心设计了区域分离注意力（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，我们的方法取得了与现有图像修复方法相当的结果，同时提供了其他方法所不具备的反取证能力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
