{"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/content-authentication-for-neural-imaging","title":"Content Authentication for Neural Imaging Pipelines: End-to-end Optimization of Photo Provenance in Complex Distribution Channels","arxiv_id":"1812.01516","date":"2018-12-04","proceeding":"CVPR 2019 6","authors":["Pawel Korus","Nasir Memon"],"abstract":"Forensic analysis of digital photo provenance relies on intrinsic traces left\nin the photograph at the time of its acquisition. Such analysis becomes\nunreliable after heavy post-processing, such as down-sampling and\nre-compression applied upon distribution in the Web. This paper explores\nend-to-end optimization of the entire image acquisition and distribution\nworkflow to facilitate reliable forensic analysis at the end of the\ndistribution channel. We demonstrate that neural imaging pipelines can be\ntrained to replace the internals of digital cameras, and jointly optimized for\nhigh-fidelity photo development and reliable provenance analysis. In our\nexperiments, the proposed approach increased image manipulation detection\naccuracy from 45% to over 90%. The findings encourage further research towards\nbuilding more reliable imaging pipelines with explicit provenance-guaranteeing\nproperties.","url_abs":"http://arxiv.org/abs/1812.01516v2","url_pdf":"http://arxiv.org/pdf/1812.01516v2.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":"content-authentication-for-neural-imaging","repo_url":"https://github.com/pkorus/neural-imaging","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"image-manipulation","task_name":"Image Manipulation"},{"task_slug":"image-manipulation-detection","task_name":"Image Manipulation Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}