Forensic similarity for source camera model comparison
Resumen:
In the article 'Forensic Similarity for Digital Images', O. Mayer and M. C. Stamm introduce the forensic similarity approach, which aims at determining whether two image patches contain the same forensic traces or not. The proposed method is based on a feed-forward neural network which consists of two modules : a feature extraction module using a pair of CNNs in a siamese configuration, and a three-layer neural network that maps the extracted features into a similarity score. In this article, we explore the use of the forensic similarity score for source camera model comparison, as one of the possible applications of such an approach suggested by Mayer and Stamm.
| 2022 | |
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Proyecto ANR-DGA (ANR-16-DEFA-0004) Proyecto vera.ai (101070093) |
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Image forensics Source camera Camera model |
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| Inglés | |
| Universidad de la República | |
| COLIBRI | |
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http://www.ipol.im/pub/art/2022/424/
https://hdl.handle.net/20.500.12008/39787 |
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| Acceso abierto | |
| Licencia Creative Commons Atribución - No Comercial - Compartir Igual (CC - By-NC-SA 4.0) |
| Sumario: | In the article 'Forensic Similarity for Digital Images', O. Mayer and M. C. Stamm introduce the forensic similarity approach, which aims at determining whether two image patches contain the same forensic traces or not. The proposed method is based on a feed-forward neural network which consists of two modules : a feature extraction module using a pair of CNNs in a siamese configuration, and a three-layer neural network that maps the extracted features into a similarity score. In this article, we explore the use of the forensic similarity score for source camera model comparison, as one of the possible applications of such an approach suggested by Mayer and Stamm. |
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