| Title | Forensic Similarity for Digital Images |
| Publication Type | Journal Article |
| Year of Publication | 2020 |
| Authors | Mayer, O., Stamm, M. C. |
| Journal | IEEE Transactions on Information Forensics and Security |
| Volume | 15 |
| Pagination | 1331—1346 |
| ISSN | 1556-6021 |
| Keywords | camera model, Cameras, convolutional neural nets, convolutional neural network-based feature extractor, database consistency verification, Deep Learning, digital image forensics approach, digital images, editing operation, feature extraction, forensic similarity approach, forensic similarity decision, forensic trace, Forensics, Forgery, forgery detection, Human Behavior, Image forensics, image patches, information forensics, learning (artificial intelligence), manipulation parameter, Metrics, multimedia forensics, pubcrawl, resilience, Resiliency, Scalability, similarity network, three-layer neural network, Training, two-part deep-learning system |
| Abstract | In this paper, we introduce a new digital image forensics approach called forensic similarity, which determines whether two image patches contain the same forensic trace or different forensic traces. One benefit of this approach is that prior knowledge, e.g., training samples, of a forensic trace is not required to make a forensic similarity decision on it in the future. To do this, we propose a two-part deep-learning system composed of a convolutional neural network-based feature extractor and a three-layer neural network, called the similarity network. This system maps the pairs of image patches to a score indicating whether they contain the same or different forensic traces. We evaluated the system accuracy of determining whether two image patches were captured by the same or different camera model and manipulated by the same or a different editing operation and the same or a different manipulation parameter, given a particular editing operation. Experiments demonstrate applicability to a variety of forensic traces and importantly show efficacy on "unknown" forensic traces that were not used to train the system. Experiments also show that the proposed system significantly improves upon prior art, reducing error rates by more than half. Furthermore, we demonstrated the utility of the forensic similarity approach in two practical applications: forgery detection and localization, and database consistency verification. |
| DOI | 10.1109/TIFS.2019.2924552 |
| Citation Key | mayer_forensic_2020 |
Forensic Similarity for Digital Images