{"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/objectformer-for-image-manipulation-detection","title":"ObjectFormer for Image Manipulation Detection and Localization","arxiv_id":"2203.14681","date":"2022-03-28","proceeding":"CVPR 2022 1","authors":["Junke Wang","Zuxuan Wu","Jingjing Chen","Xintong Han","Abhinav Shrivastava","Ser-Nam Lim","Yu-Gang Jiang"],"abstract":"Recent advances in image editing techniques have posed serious challenges to the trustworthiness of multimedia data, which drives the research of image tampering detection. In this paper, we propose ObjectFormer to detect and localize image manipulations. To capture subtle manipulation traces that are no longer visible in the RGB domain, we extract high-frequency features of the images and combine them with RGB features as multimodal patch embeddings. Additionally, we use a set of learnable object prototypes as mid-level representations to model the object-level consistencies among different regions, which are further used to refine patch embeddings to capture the patch-level consistencies. We conduct extensive experiments on various datasets and the results verify the effectiveness of the proposed method, outperforming state-of-the-art tampering detection and localization methods.","url_abs":"https://arxiv.org/abs/2203.14681v2","url_pdf":"https://arxiv.org/pdf/2203.14681v2.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":[],"tasks":[{"task_slug":"image-manipulation","task_name":"Image Manipulation"},{"task_slug":"image-manipulation-detection","task_name":"Image Manipulation Detection"},{"task_slug":"image-manipulation-localization","task_name":"Image Manipulation Localization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-manipulation-localization-on-casiav1","task":"Image Manipulation Localization","dataset":"CASIAv1(Protoclo-CAT)","model":"ObjectFormer","rank_in_archive_order":7,"of":8,"metrics":{"Pixel Binary F1":"0.531"},"uses_additional_data":false},{"leaderboard":"/sota/image-manipulation-localization-on-coverage-1","task":"Image Manipulation Localization","dataset":"COVERAGE(Protocol-CAT)","model":"ObjectFormer","rank_in_archive_order":7,"of":8,"metrics":{"Pixel Binary F1":"0.257"},"uses_additional_data":false},{"leaderboard":"/sota/image-manipulation-localization-on-columbia-1","task":"Image Manipulation Localization","dataset":"Columbia(Protocol-CAT)","model":"ObjectFormer","rank_in_archive_order":7,"of":8,"metrics":{"Pixel Binary F1":"0.732"},"uses_additional_data":false},{"leaderboard":"/sota/image-manipulation-localization-on-nist16","task":"Image Manipulation Localization","dataset":"NIST16(Protocol-CAT)","model":"ObjectFormer","rank_in_archive_order":7,"of":8,"metrics":{"Pixel Binary F1":"0.252"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2203.14681","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}