Exposing Fake Images with Forensic Similarity Graphs

Abstract

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 abstract, 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.

16 pages, under review at IEEE Journal of Selected Topics in Signal Processing

References in corpus (10)

• Fast unfolding of communities in large networks
• Near linear time algorithm to detect community structures in large-scale networks
• Resolution limit in community detection
• Statistical Mechanics of Community Detection
• Media Forensics and DeepFakes: an overview
• Hybrid LSTM and Encoder-Decoder Architecture for Detection of Image Forgeries
• Image Splicing Localization Using A Multi-Task Fully Convolutional Network (MFCN)
• Aligned and Non-Aligned Double JPEG Detection Using Convolutional Neural Networks
• Forensic Similarity for Digital Images
• Detecting Photoshopped Faces by Scripting Photoshop

Cited by in corpus (2)

• Media Forensics and DeepFakes: an overview
• Image Splicing Detection, Localization and Attribution via JPEG Primary Quantization Matrix Estimation and Clustering