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