{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T01:49:01Z","timestamp":1769219341560,"version":"3.49.0"},"reference-count":0,"publisher":"IBERAMIA: Sociedad Iberoamericana de Inteligencia Artificial","issue":"73","license":[{"start":{"date-parts":[[2024,1,5]],"date-time":"2024-01-05T00:00:00Z","timestamp":1704412800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ia"],"abstract":"<jats:p>With the rise of high-quality forged images on social media and other platforms, there is a need for algorithms that can recognize the originality. Detecting copy-move forgery is essential for ensuring the authenticity and integrity of digital images, preventing fraud and deception, and upholding the law. Copy-move forgery is the act of duplicating and pasting a portion of an image to another location within the same image. To address these issues, we propose two deep learning approaches - one using a custom architecture and the other using transfer learning. We test our method against a number of benchmark datasets and demonstrate that, in terms of accuracy and robustness against various types of image distortions, it outperforms current state-of-the-art methods. Our proposed method has applications in digital forensics, copyright defence, and image authenticity.<\/jats:p>","DOI":"10.4114\/intartif.vol27iss73pp80-91","type":"journal-article","created":{"date-parts":[[2024,1,5]],"date-time":"2024-01-05T02:44:55Z","timestamp":1704422695000},"page":"80-91","source":"Crossref","is-referenced-by-count":6,"title":["CNN-based Approach for Robust Detection of Copy-Move Forgery in Images"],"prefix":"10.4114","volume":"27","author":[{"given":"Arivazhagan","family":"S","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Newlin Shebiah","family":"Russel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saranyaa","family":"M","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shanmuga Priya","family":"R","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"2598","published-online":{"date-parts":[[2024,1,5]]},"container-title":["Inteligencia Artificial"],"original-title":[],"link":[{"URL":"http:\/\/journal.iberamia.org\/index.php\/intartif\/article\/download\/1078\/212","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/journal.iberamia.org\/index.php\/intartif\/article\/download\/1078\/212","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,15]],"date-time":"2024-01-15T02:56:33Z","timestamp":1705287393000},"score":1,"resource":{"primary":{"URL":"http:\/\/journal.iberamia.org\/index.php\/intartif\/article\/view\/1078"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,5]]},"references-count":0,"journal-issue":{"issue":"73","published-online":{"date-parts":[[2024,1,5]]}},"URL":"https:\/\/doi.org\/10.4114\/intartif.vol27iss73pp80-91","relation":{},"ISSN":["1988-3064","1137-3601"],"issn-type":[{"value":"1988-3064","type":"electronic"},{"value":"1137-3601","type":"print"}],"subject":[],"published":{"date-parts":[[2024,1,5]]}}}