Abstract. Diffusion-based inpainting can be used to repair some damaged parts or remove the undesirable regions in an image. Generally, good visual effects can be achieved after inpainting. However, some traces, such as the differences of local variances and noise pattern, are left in the inpainted image, making it easy for the forensic algorithms to locate the inpainted regions. To eliminate this drawback and achieve an anti-forensics capability, we propose an approach that can remove the traces of the diffusion-based inpainting. Since the pixel values of the inpainted regions are diffused inward by the surrounding pixels, we first analyze the noise pattern of the pixels neighboring the inpainted regions and select the nearby pixels that are directly used for inpainting. After that, a statistical probability model is constructed for each channel in the image, which is used to generate the noise pattern and fill the inpainted regions. Experimental results show that the proposed approach has a good capability for anti-forensics.

中文翻译：

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基于扩散的图像修复的反取证

摘要。基于扩散的修复可用于修复某些损坏的部分或去除图像中不需要的区域。一般来说，修补后可以达到良好的视觉效果。然而，一些痕迹，如局部方差和噪声模式的差异，留在了修复图像中，使得取证算法很容易定位修复区域。为了消除这个缺点并实现反取证能力，我们提出了一种可以去除基于扩散的修复痕迹的方法。由于修复区域的像素值被周围像素向内扩散，我们首先分析邻近修复区域的像素的噪声模式，并选择直接用于修复的附近像素。之后，为图像中的每个通道构建一个统计概率模型，用于生成噪声模式并填充修复区域。实验结果表明，该方法具有良好的反取证能力。