Deep Studying Signature for Obstruction obscure in Copy Move Image Forgeries

Deep Studying Signature for Obstruction obscure in Copy Move Image Forgeries
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© 2022 by IJETT Journal
Volume-70 Issue-10
Year of Publication : 2022
Authors : S. Shashikala, G. K. Ravikumar
DOI : 10.14445/22315381/IJETT-V70I10P225

How to Cite?

S. Shashikala, G. K. Ravikumar, "Deep Studying Signature for Obstruction obscure in Copy Move Image Forgeries," International Journal of Engineering Trends and Technology, vol. 70, no. 10, pp. 262-270, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I10P225

Abstract
Digital image tampering and counterfeiting can be done precisely with advanced photo editing tools available with various malicious intentions. It becomes necessary to verify the integrity of the image as images are becoming the information source in various computer-aided applications. Copy move counterfeits are created by copying a slice from one region of the image to another region. The current techniques for detecting copy-move counterfeits fail in the presence of partial occlusion or partial distortion created to falsify detection. This work proposes a deep learning signature to solve this problem. Deep learning signature is created using a probabilistic distribution function of occlusions. The coarse forgery regions are detected with scale-invariant feature transform-based keypoint matching. Deep learning signature matches the coarse forgery regions to detect the partially occluded copy move counterfeits.

Keywords
Copy-move forgery, Coarse forgery regions, Deep learning signature, Partially occluded counterfeit.

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