{"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-splicing-localization-using-a-multi","title":"Image Splicing Localization Using A Multi-Task Fully Convolutional Network (MFCN)","arxiv_id":"1709.02016","date":"2017-09-06","proceeding":null,"authors":["Ronald Salloum","Yuzhuo Ren","C. -C. Jay Kuo"],"abstract":"In this work, we propose a technique that utilizes a fully convolutional\nnetwork (FCN) to localize image splicing attacks. We first evaluated a\nsingle-task FCN (SFCN) trained only on the surface label. Although the SFCN is\nshown to provide superior performance over existing methods, it still provides\na coarse localization output in certain cases. Therefore, we propose the use of\na multi-task FCN (MFCN) that utilizes two output branches for multi-task\nlearning. One branch is used to learn the surface label, while the other branch\nis used to learn the edge or boundary of the spliced region. We trained the\nnetworks using the CASIA v2.0 dataset, and tested the trained models on the\nCASIA v1.0, Columbia Uncompressed, Carvalho, and the DARPA/NIST Nimble\nChallenge 2016 SCI datasets. Experiments show that the SFCN and MFCN outperform\nexisting splicing localization algorithms, and that the MFCN can achieve finer\nlocalization than the SFCN.","url_abs":"http://arxiv.org/abs/1709.02016v1","url_pdf":"http://arxiv.org/pdf/1709.02016v1.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-splicing-localization-using-a-multi","repo_url":"https://github.com/namtpham/image_tampering_detection_references","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"multi-task-learning","task_name":"Multi-Task Learning"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"fcn","method_name":"FCN"},{"method_slug":"max-pooling","method_name":"Max Pooling"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1709.02016","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}