Image Forgery Detection by Transforming Local Descriptors into Deep-Derived Features

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Image Forgery Detection by Transforming Local Descriptors into Deep-Derived Features 33 Pages Posted: 11 Jun 2022 See all articles by Muhammad Aqib AnwarMuhammad Aqib AnwarGovernment of the United States of America - Air UniversitySyed Fahad TahirGovernment of the United States of America - Air UniversityLabiba Gillani Fahadaffiliation not provided to SSRNKashif Kifayat KifayatGovernment of the United States of America - Air University Abstract Image forgery is the intentional alteration of digital images, either manually using image editors or through deep fake techniques, for the purpose of disseminating fake information. We propose a forgery detection approach that efficiently detects copy-move and splicing attacks of varying scales in digital images. We aim to identify the homogeneous region(s) inconsistent with the rest of the image. This region property has also been traditionally exploited in object detection and classification. Thus, we generate the deep-derived features by exploiting the existing hand-crafted features in forgery detection as input to the VGG16, a deep learning method, trained for object classification. We classify an image as real or fake using the binary class SVM trained on the obtained deep features. We evaluate the proposed approach on three publicly available image manipulation datasets, DVMM, Casia and Korus. The results show a better accuracy compared to the state-of-the-art methods. Keywords: forgery detection, local descriptors, deep derived features, support vector machine Suggested Citation: Suggested Citation Anwar, Muhammad Aqib and Tahir, Syed Fahad and Fahad, Labiba Gillani and Kifayat, Kashif Kifayat, Image Forgery Detection by Transforming Local Descriptors into Deep-Derived Features. Available at SSRN: https://ssrn.com/abstract=4134079 Muhammad Aqib Anwar (Contact Author) Government of the United States of America - Air University ( email ) United States Syed Fahad Tahir Government of the United States of America - Air University ( email ) United States Labiba Gillani Fahad affiliation not provided to SSRN ( email ) No Address Available Kashif Kifayat Kifayat Government of the United States of America - Air University ( email ) United States Download This Paper Open PDF in Browser Do you have a job opening that you would like to promote on SSRN? Place Job Opening Paper statistics Downloads 0 Abstract Views 9 PlumX Metrics Related eJournals Cybersecurity, Privacy, & Networks eJournal Follow Cybersecurity, Privacy, & Networks eJournal Subscribe to this fee journal for more curated articles on this topic FOLLOWERS 272 PAPERS 2,711 Feedback Feedback to SSRN Feedback (required) Email (required) Submit If you need immediate assistance, call 877-SSRNHelp (877 777 6435) in the United States, or +1 212 448 2500 outside of the United States, 8:30AM to 6:00PM U.S. Eastern, Monday - Friday. Submit a Paper Section 508 Text Only Pages SSRN Quick Links SSRN Solutions Research Paper Series Conference Papers Partners in Publishing Jobs & Announcements Newsletter Sign Up SSRN Rankings Top Papers Top Authors Top Organizations About SSRN SSRN Objectives Network Directors Presidential Letter Announcements Contact us FAQs Copyright Terms and Conditions Privacy Policy We use cookies to help provide and enhance our service and tailor content. To learn more, visit Cookie Settings. This page was processed by aws-apollo5 in 0.266 seconds