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Manipulation tools that realistically edit images are widely available,making it easy for anyone to create and spread misinformation. In an attempt tofight fake news, forgery detection and localization methods were designed.However, existing methods struggle to accurately reveal manipulations found inimages on the internet, i.e., in the wild. That is because the type of forgeryis typically unknown, in addition to the tampering traces being damaged byrecompression. This paper presents Comprint, a novel forgery detection andlocalization method based on the compression fingerprint or comprint. It istrained on pristine data only, providing generalization to detect differenttypes of manipulation. Additionally, we propose a fusion of Comprint with thestate-of-the-art Noiseprint, which utilizes a complementary camera modelfingerprint. We carry out an extensive experimental analysis and demonstratethat Comprint has a high level of accuracy on five evaluation datasets thatrepresent a wide range of manipulation types, mimicking in-the-wildcircumstances. Most notably, the proposed fusion significantly outperformsstate-of-the-art reference methods. As such, Comprint and the fusionComprint+Noiseprint represent a promising forensics tool to analyze in-the-wildtampered images.

Comprint: Image Forgery Detection and Localization using Compression Fingerprints