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CNN-based Approach for Robust Detection of Copy-Move Forgery in Images

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24510‎$a‎CNN-based Approach for Robust Detection of Copy-Move Forgery in Images‎$c‎Arivazhagan S. [et al.]
520  ‎$a‎With 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
650 4‎$0‎MAPA20080541064‎$a‎Fraude
650 4‎$0‎MAPA20080545932‎$a‎Análisis
650 4‎$0‎MAPA20080541408‎$a‎Imagen
650 4‎$0‎MAPA20080579975‎$a‎Derechos de autor
650 4‎$0‎MAPA20240002428‎$a‎Autentificación biométrica
650 4‎$0‎MAPA20080611200‎$a‎Inteligencia artificial
7001 ‎$0‎MAPA20240020873‎$a‎S., Arivazhagan
7730 ‎$w‎MAP20200034445‎$g‎19/06/2024 Volumen 27 Número 73 - junio 2024 , p.80-91‎$x‎1988-3064‎$t‎Revista Iberoamericana de Inteligencia Artificial‎$d‎ : IBERAMIA, Sociedad Iberoamericana de Inteligencia Artificial , 2018-
856  ‎$u‎https://journal.iberamia.org/index.php/intartif/article/view/1078