A robust and forensic transform for copy move digital image forgery detection based on dense depth block matching.

Copy-move forgery is one of the most popular tampering artefacts in digital images. However, tampering effect in digital images makes the authentication of the processing as untrustworthy. In this paper, a combination of Fourier-Mellin and Zernike moments (FMZM) Transform is proposed which detects the copy-move region with high-speed and low-computational complexity. Here, initially an image is segmented into various blocks using marker controlled watershed management and from that proposed FMZM feature extraction is used which detects duplication. The detected regions are matched with the Dense Depth Reconstruction based lexicographically sorting. Finally, tampered outliers presented at the data are removed through RANSAC (RANdom Sample Consensus) algorithm, in which removed false matches are verified with the morphological operators. The efficiency of proposed method is measured by various performance metrics and this method earned up to 97.56%, 99.98%, and 97.12% for precision, recall, and F1-score performance, respectively. [ABSTRACT FROM AUTHOR]