In this paper, we propose new image forgery detection and localization algorithms by recasting these problems as graph-based community detection problems. To do this, we introduce a novel graph-based representation of an image, which we call the Forensic Similarity Graph, that captures key forensic relationships among regions in the image. In this representation, small image patches are represented by graph vertices with edges assigned according to the forensic similarity between patches. Localized tampering introduces unique structure into this graph, which aligns with a concept called “community structure” in graph-theory literature. In the Forensic Similarity Graph, communities correspond to the tampered and unaltered regions in the image. As a result, forgery detection is performed by identifying whether multiple communities exist, and forgery localization is performed by partitioning these communities. We present two community detection techniques, adapted from literature, to detect and localize image forgeries. We experimentally show that our proposed community detection methods outperform existing state-of-the-art forgery detection and localization methods, which do not capture such community structure.

中文翻译：

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用取证相似图揭露假图像


在本文中，我们通过将这些问题重新定义为基于图的社区检测问题，提出了新的图像伪造检测和定位算法。为此，我们引入了一种新颖的基于图形的图像表示，我们将其称为取证相似性图，它捕获图像中区域之间的关键取证关系。在这种表示中，小图像块由图顶点表示，其边缘根据块之间的取证相似性分配。局部篡改在该图中引入了独特的结构，这与图论文献中称为“社区结构”的概念相一致。在取证相似度图中，社区对应于图像中被篡改和未更改的区域。结果，通过识别是否存在多个社区来执行伪造检测，并且通过划分这些社区来执行伪造定位。我们提出了两种改编自文献的社区检测技术，用于检测和定位图像伪造。我们通过实验表明，我们提出的社区检测方法优于现有最先进的伪造检测和定位方法，这些方法无法捕获此类社区结构。