We are happy that our paper titled “Comprint: Image Forgery Detection and Localization using Compression Fingerprints” was accepted at the Workshop on MultiMedia Forensics in the WILD (MMForWILD) 2022, held in conjunction with the Int. Conference on Pattern Recognition (ICPR) 2022. This work was made in collaboration with the Image Processing Research Group of the University of Napels Federico II, Italy.
The work was presented on 21 August 2022. A recorded version of the 18-minute presentation can be found below, or by clicking here.
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.
Our 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.
An image is transformed to a comprint or compression fingerprint, that can be used to create a heatmap that detects and localizes the forgeries.
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.
The source code of Comprint is available on GitHub.com.