KAZE Feature Based Passive Image Forgery Detection
Copy-move image forgery is a most common tampering artifact. It can be carried out by copy-pasting a region of the same image; thus, it has become a challenging one to find. So, this paper put forwarding a method to detect such forgery by extracting the KAZE features. RANSAC algorithm is functioned to get rid off the false matches such as outliers, and then the forged image is disclosed. The experiment is carried out using the publically available datasets, and their performances are quantitatively assessed using the true positive rate and false positive rate. A comparative analysis is also done with state-of-the-art methods, and it is certified that the proposed method produced good results than the other methods.
KeywordsCopy-move forgery KAZE RANSAC Clustering TPR FPR
The authors express their gratitude and credits for the use of the MICC-F220 and MICC-F2000 databases.
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