Passive copy move image forgery detection using undecimated dyadic wavelet transform

In this paper, a blind copy move image forgery detection method using undecimated dyadic wavelet transform (DyWT) is proposed. DyWT is shift invariant and therefore more suitable than discrete wavelet transform (DWT) for data analysis. First, the input image is decomposed into approximation (LL1) and detail (HH1) subbands. Then the LL1 and HH1 subbands are divided into overlapping blocks and the similarity between blocks is calculated. The key idea is that the similarity between the copied and moved blocks from the LL1 subband should be high, while that from the HH1 subband should be low due to noise inconsistency in the moved block. Therefore, pairs of blocks are sorted based on high similarity using the LL1 subband and high dissimilarity using the HH1 subband. Using thresholding, matched pairs are obtained from the sorted list as copied and moved blocks. Experimental results show the effectiveness of the proposed method over competitive methods using DWT and the LL1 or HH1 subbands only.