Xueyan Li
ABSTRACT
In order to reduce the complexity of forgery detection algorithm and improve the accuracy, this paper proposes an image forgery detection algorithm based on DCT coupled random sample consensus optimization. First of all, the initial image is divided into sub-blocks of uniform size and DCT coefficients for each block is obtained through DCT to represent each blocks; then, circular feature screening mechanism is established to extract four features of the block, thereby reducing the feature dimension of each block. Finally, each eigenvector is ordered in a lexicographical manner and prior threshold is used to match the feature, reduce the image block false matching rate optimized by random sample consensus, thus completing the image authenticity for decision making. Experimental results show that, compared with the current image forgery detection algorithm, this algorithm has better robustness, efficiency and accuracy, and good detection effects on the fuzzy and noise forgery.
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Index Terms
- Image Authenticity Decision Based on Random Sample Consensus and Circular Feature Selection
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