Cloning Localization Based on Feature Extraction and K-means Clustering
The field of image forensics is expanding rapidly. Many passive image tamper detection techniques have been presented. Some of these techniques use feature extraction methods for tamper detection and localization. This work is based on extracting Maximally Stable Extremal Regions (MSER) features for cloning detection, followed by k-means clustering for cloning localization. Then for comparison purposes, we implement the same approach using Speeded Up Robust Features (SURF) and Scale-Invariant Feature Transform (SIFT). Experimental results show that we can detect and localize cloning in tampered images with an accuracy reaching 97 % using MSER features. The usability and efficacy of our approach is verified by comparing with recent state-of-the-art approaches.
KeywordsCloning localization MSER features SIFT SURF K-means clustering
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