{"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/image-provenance-analysis-at-scale","title":"Image Provenance Analysis at Scale","arxiv_id":"1801.06510","date":"2018-01-19","proceeding":null,"authors":["Daniel Moreira","Aparna Bharati","Joel Brogan","Allan Pinto","Michael Parowski","Kevin W. Bowyer","Patrick J. Flynn","Anderson Rocha","Walter J. Scheirer"],"abstract":"Prior art has shown it is possible to estimate, through image processing and\ncomputer vision techniques, the types and parameters of transformations that\nhave been applied to the content of individual images to obtain new images.\nGiven a large corpus of images and a query image, an interesting further step\nis to retrieve the set of original images whose content is present in the query\nimage, as well as the detailed sequences of transformations that yield the\nquery image given the original images. This is a problem that recently has\nreceived the name of image provenance analysis. In these times of public media\nmanipulation ( e.g., fake news and meme sharing), obtaining the history of\nimage transformations is relevant for fact checking and authorship\nverification, among many other applications. This article presents an\nend-to-end processing pipeline for image provenance analysis, which works at\nreal-world scale. It employs a cutting-edge image filtering solution that is\ncustom-tailored for the problem at hand, as well as novel techniques for\nobtaining the provenance graph that expresses how the images, as nodes, are\nancestrally connected. A comprehensive set of experiments for each stage of the\npipeline is provided, comparing the proposed solution with state-of-the-art\nresults, employing previously published datasets. In addition, this work\nintroduces a new dataset of real-world provenance cases from the social media\nsite Reddit, along with baseline results.","url_abs":"http://arxiv.org/abs/1801.06510v2","url_pdf":"http://arxiv.org/pdf/1801.06510v2.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":"image-provenance-analysis-at-scale","repo_url":"https://github.com/cvrl/scalable_provenance","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"authorship-verification","task_name":"Authorship Verification"},{"task_slug":"fact-checking","task_name":"Fact Checking"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1801.06510","atlas_url":"https://app.syntology.ai/?focus=1801.06510","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}