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CNN-based Approach for Robust Detection of Copy-Move Forgery in Images
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MAP20240013301
CNN-based Approach for Robust Detection of Copy-Move Forgery in Images / Arivazhagan S. [et al.]
Sumario: 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
En: Revista Iberoamericana de Inteligencia Artificial. - : IBERAMIA, Sociedad Iberoamericana de Inteligencia Artificial , 2018- = ISSN 1988-3064. - 19/06/2024 Volumen 27 Número 73 - junio 2024 , p.80-91
1. Fraude ‌. 2. Análisis ‌. 3. Imagen ‌. 4. Derechos de autor ‌. 5. Autentificación biométrica ‌. 6. Inteligencia artificial ‌. I. S., Arivazhagan ‌. II. Title.