PROJECT TITLE :

Copy-Paste Detection Based On A Sift Marked Graph Feature Vector - 2017

ABSTRACT:

To detect copy-paste tampering, an improved SIFT (Scale invariant feature remodel)-based algorithm was proposed. Maximum angle is defined and a most angle-based marked graph is constructed. The marked graph feature vector is provided to each SIFT key point via discrete polar coordinate transformation. Key points are matched to detect the copy-paste tampering regions. The experimental results show that the proposed algorithm will effectively determine and detect the rotated or scaled copy-paste regions, and in comparison with the strategies reported previously, it is resistant to postprocessing, like blurring, Gaussian white noise and JPEG recompression. The proposed algorithm performs better than the prevailing algorithm to managing scaling transformation.


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