{"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/fighting-fake-news-image-splice-detection-via","title":"Fighting Fake News: Image Splice Detection via Learned Self-Consistency","arxiv_id":"1805.04096","date":"2018-05-10","proceeding":"ECCV 2018 9","authors":["Minyoung Huh","Andrew Liu","Andrew Owens","Alexei A. Efros"],"abstract":"Advances in photo editing and manipulation tools have made it significantly\neasier to create fake imagery. Learning to detect such manipulations, however,\nremains a challenging problem due to the lack of sufficient amounts of\nmanipulated training data. In this paper, we propose a learning algorithm for\ndetecting visual image manipulations that is trained only using a large dataset\nof real photographs. The algorithm uses the automatically recorded photo EXIF\nmetadata as supervisory signal for training a model to determine whether an\nimage is self-consistent -- that is, whether its content could have been\nproduced by a single imaging pipeline. We apply this self-consistency model to\nthe task of detecting and localizing image splices. The proposed method obtains\nstate-of-the-art performance on several image forensics benchmarks, despite\nnever seeing any manipulated images at training. That said, it is merely a step\nin the long quest for a truly general purpose visual forensics tool.","url_abs":"http://arxiv.org/abs/1805.04096v3","url_pdf":"http://arxiv.org/pdf/1805.04096v3.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":"fighting-fake-news-image-splice-detection-via","repo_url":"https://github.com/minyoungg/selfconsistency","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"fighting-fake-news-image-splice-detection-via","repo_url":"https://github.com/shauryagoel/Image-Splice-Detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"fighting-fake-news-image-splice-detection-via","repo_url":"https://github.com/yizhe-ang/fake-detection-lab","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"image-forensics","task_name":"Image Forensics"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1805.04096","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.04096"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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