Abstract
Block-based image copy-move detection algorithms disregard the spatial layout of the features, leading to the poor detection performance under small-region tampering samples. Therefore, we propose a pyramid correlation network (PCNet) for copy-move forgery detection, whose goal is to obtain rich and detailed image representation via a pyramid cascaded correlation architecture. Experimental results show that PCNet outperforms the comparison algorithm on USCISI, CASIA and CoMoFoD data sets. Compared to the benchmark model BusterNet, F1 scores of PCNet has increased by 33.84% and 30.62% on CASIA CMFD dataset and CoMoFoD dataset respectively.
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