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

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      <subfield code="a">CNN-based Approach for Robust Detection of Copy-Move Forgery in Images</subfield>
      <subfield code="c">Arivazhagan S. [et al.]</subfield>
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      <subfield code="a">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</subfield>
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      <subfield code="g">19/06/2024 Volumen 27 Número 73 - junio 2024 , p.80-91</subfield>
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      <subfield code="t">Revista Iberoamericana de Inteligencia Artificial</subfield>
      <subfield code="d"> : IBERAMIA, Sociedad Iberoamericana de Inteligencia Artificial , 2018-</subfield>
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