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

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<dc:creator>S., Arivazhagan </dc:creator>
<dc:date>2024-06-19</dc:date>
<dc:description xml:lang="es">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</dc:description>
<dc:identifier>https://documentacion.fundacionmapfre.org/documentacion/publico/es/bib/186116.do</dc:identifier>
<dc:language>eng</dc:language>
<dc:rights xml:lang="es">InC - http://rightsstatements.org/vocab/InC/1.0/</dc:rights>
<dc:subject xml:lang="es">Fraude</dc:subject>
<dc:subject xml:lang="es">Análisis</dc:subject>
<dc:subject xml:lang="es">Imagen</dc:subject>
<dc:subject xml:lang="es">Derechos de autor</dc:subject>
<dc:subject xml:lang="es">Autentificación biométrica</dc:subject>
<dc:subject xml:lang="es">Inteligencia artificial</dc:subject>
<dc:type xml:lang="es">Artículos y capítulos</dc:type>
<dc:title xml:lang="es">CNN-based Approach for Robust Detection of Copy-Move Forgery in Images</dc:title>
<dc:relation xml:lang="es">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</dc:relation>
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