Abstract
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.
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Notes
- 1.
Code available on https://github.com/IDLabMedia/comprint.
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Acknowledgements
This work was funded in part by the Research Foundation – Flanders (FWO) under Grant V414022N, IDLab (Ghent University – imec), Flanders Innovation & Entrepreneurship (VLAIO), and the European Union. In addition, this material is based on research sponsored by the Defense Advanced Research Projects Agency (DARPA) and the Air Force Research Laboratory (AFRL) under agreement number FA8750-20-2-1004. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of DARPA and AFRL or the U.S. Government. This work is also supported by a Google gift and by the PREMIER project, funded by the Italian Ministry of Education, University, and Research within the PRIN 2017 program.
The computational resources (imec iLabt & STEVIN Supercomputer Infrastructure) and services used in this work were kindly provided by Ghent University, imec, the Flemish Supercomputer Center (VSC), the Hercules Foundation, the Flemish Government department EWI, as well as by University Federico II of Naples.
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Mareen, H. et al. (2023). Comprint: Image Forgery Detection and Localization Using Compression Fingerprints. In: Rousseau, JJ., Kapralos, B. (eds) Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges. ICPR 2022. Lecture Notes in Computer Science, vol 13644. Springer, Cham. https://doi.org/10.1007/978-3-031-37742-6_23
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DOI: https://doi.org/10.1007/978-3-031-37742-6_23
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