{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/prnu-based-source-camera-attribution-for","title":"PRNU Based Source Camera Attribution for Image Sets Anonymized with Patch-Match Algorithm","arxiv_id":"1906.11871","date":"2019-06-27","proceeding":null,"authors":[],"abstract":"Patch-Match is an efficient algorithm used for structural image editing and\navailable as a tool on popular commercial photo-editing software. The tool\nallows users to insert or remove objects from photos using information from\nsimilar scene content. Recently, a modified version of this algorithm was\nproposed as a counter-measure against Photo-Response Non-Uniformity (PRNU)\nbased Source Camera Identification (SCI). The algorithm can provide anonymity\nat a great rate (97\\%) and impede PRNU based SCI without the need of any other\ninformation, hence leaving no-known recourse for the PRNU-based SCI. In this\npaper, we propose a method to identify sources of the Patch-Match-applied\nimages by using randomized subsets of images and the traditional PRNU based SCI\nmethods. We evaluate the proposed method on two forensics scenarios in which an\nadversary makes use of the Patch-Match algorithm and distorts the PRNU noise\npattern in the incriminating images he took with his camera. Our results show\nthat it is possible to link sets of Patch-Match-applied images back to their\nsource camera even in the presence of images that come from unknown cameras. To\nour best knowledge, the proposed method represents the very first\ncounter-measure against the usage of of Patch-Match in the digital forensics\nliterature.","url_abs":"http://arxiv.org/abs/1906.11871v1","url_pdf":"http://arxiv.org/pdf/1906.11871v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"prnu-based-source-camera-attribution-for","repo_url":"https://github.com/akarakucuk/2019_PM_SCI_DATA","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}