Detection of Image Forgery Based on Improved PCA-SIFT
In view of the problem existing in abusive using of image copy-move forgeries, this paper proposes an image forensics algorithm for detecting copy-move forgery based on improved PCA-SIFT. The present method works first by extracting features of an image and then reducing its dimensionality, and the method uses k-nearest neighbor to operate forgery detection. Owing to the similarity between pasted region and copied region, the descriptors are then matched between each other to seek for any possible forgery in images. Extensive experimental results are presented to confirm that the algorithm is able to precisely individuate the tampered image and quantify its robustness and sensitivity to image post-processing and offer a considerable improvement in time efficiency.
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