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

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<title>CNN-based Approach for Robust Detection of Copy-Move Forgery in Images</title>
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<namePart>S., Arivazhagan </namePart>
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<abstract displayLabel="Summary">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</abstract>
<note type="statement of responsibility">Arivazhagan S. [et al.]</note>
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<url displayLabel="electronic resource" usage="primary display">https://journal.iberamia.org/index.php/intartif/article/view/1078</url>
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<title>Revista Iberoamericana de Inteligencia Artificial</title>
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<publisher> : IBERAMIA, Sociedad Iberoamericana de Inteligencia Artificial , 2018-</publisher>
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<identifier type="issn">1988-3064</identifier>
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<text>19/06/2024 Volumen 27 Número 73 - junio 2024 , p.80-91</text>
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