CNN-based Approach for Robust Detection of Copy-Move Forgery in Images
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001 | MAP20240013301 | ||
003 | MAP | ||
005 | 20240829122008.0 | ||
008 | 240829e20240619esp|||p |0|||b|eng d | ||
040 | $aMAP$bspa$dMAP | ||
084 | $a922.131 | ||
245 | 1 | 0 | $aCNN-based Approach for Robust Detection of Copy-Move Forgery in Images$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 | |
700 | 1 | $0MAPA20240020873$aS., Arivazhagan | |
773 | 0 | $wMAP20200034445$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 |