Robust and accurate detection of image copy-move forgery using PCET-SVD and histogram of block similarity measures

Many block-based detection methods for image copy-move forgery have been reported. However, their performance degrades significantly under different geometric attacks such as rotation and scaling. In this paper, we propose a novel robust and accurate detection scheme for image copy-move forgery. It mainly consists of three steps: firstly, a suspicious image is divided into overlapping circular blocks, and polar complex exponential transform (PCET) is employed to extract geometric invariant feature of each block. Next, singular value decomposition (SVD) is applied to the coefficient matrix composed of extracted geometric invariant moments for dimension reduction. Meanwhile, the histogram of block similarity measures is adopted to estimate the optimal similarity threshold. Finally, the calculated similarity threshold is used for block matching process and consequently more accurate tampered areas are obtained. Experimental results on various datasets show that the proposed image copy-move detection approach outperforms other existing methods in the aspect of resisting geometric rotation and scaling attacks, with the adaptability of similarity threshold and low computational complexity.