{"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/boundary-based-image-forgery-detection-by","title":"Boundary-based Image Forgery Detection by Fast Shallow CNN","arxiv_id":"1801.06732","date":"2018-01-20","proceeding":null,"authors":["Zhongping Zhang","Yixuan Zhang","Zheng Zhou","Jiebo Luo"],"abstract":"Image forgery detection is the task of detecting and localizing forged parts\nin tampered images. Previous works mostly focus on high resolution images using\ntraces of resampling features, demosaicing features or sharpness of edges.\nHowever, a good detection method should also be applicable to low resolution\nimages because compressed or resized images are common these days. To this end,\nwe propose a Shallow Convolutional Neural Network(SCNN), capable of\ndistinguishing the boundaries of forged regions from original edges in low\nresolution images. SCNN is designed to utilize the information of chroma and\nsaturation. Based on SCNN, two approaches that are named Sliding Windows\nDetection (SWD) and Fast SCNN, respectively, are developed to detect and\nlocalize image forgery region. In this paper, we substantiate that Fast SCNN\ncan detect drastic change of chroma and saturation. In image forgery detection\nexperiments Our model is evaluated on the CASIA 2.0 dataset. The results show\nthat Fast SCNN performs well on low resolution images and achieves significant\nimprovements over the state-of-the-art.","url_abs":"http://arxiv.org/abs/1801.06732v2","url_pdf":"http://arxiv.org/pdf/1801.06732v2.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":"boundary-based-image-forgery-detection-by","repo_url":"https://github.com/pruthvip98/Image-Forgery-Detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"demosaicking","task_name":"Demosaicking"},{"task_slug":"image-forgery-detection","task_name":"Image Forgery Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}