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Recapture detection technique based on edge-types by analysing high-frequency components in digital images acquired through LCD screens

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Digital images are part of our lives but with the advancement of technology, the authenticity of images is in doubt. Image editing tools are used to tamper images and high-quality cameras are used to recapture tampered images to evade tamper detection. In general, tampering introduces artifacts in images and these artifacts are camouflaged by the re-acquisition process. The re-acquisition process makes forged image more like original which is hard to detect visually and statistically. Thus, existing forensic tools and techniques fail to detect tampering in reacquired or recaptured images. This paper proposes a novel technique to detect recaptured images by exploiting the high-level details present in images and based on that edge profile is obtained. Further, edges are classified into different groups. It has been observed that the number of edge pixels in these edge groups is different for original and recaptured images. Based on the number of pixels in edges, a feature vector is built and a system is trained using SVM classifier. The proposed method tested on two databases. The experimental results demonstrated that proposed method is better than existing techniques for recapture detection.

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(Portions of) the research in this paper used the ROSE Recaptured Image Dataset [5] made available by the ROSE Lab at the Nanyang Technological University, Singapore.

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Correspondence to Areesha Anjum.

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Anjum, A., Islam, S. Recapture detection technique based on edge-types by analysing high-frequency components in digital images acquired through LCD screens. Multimed Tools Appl 79, 6965–6985 (2020).

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