Multi-scale super-pixels-based passive forensics for copy-move forgery using local characteristics

Abstract

Copy-move forgery is the most common kind of tampering technique for digital images. This paper presents a novel hybrid approach, which uses the Speeded-Up Robust Features (SURF) point and characteristics of Local Feature Regions (LFRs) matching. First, the multi-scale super-pixels algorithm adaptively divides the suspect image into irregular blocks according to the texture level of host images. Then, the improved SURF detector is adopted in extracting feature points from each super-pixel and the feature point threshold is related to the entropy of each super-pixel block. Next, LFRs are defined, and a robust feature descriptor is extracted from each LFR as a vector field. Last, the matching LFRs are found by using Euclidean locality sensitive hashing; the removal of falsely matched pairs is realised by using the Random Sample Consensus (RANSAC) algorithm. Comparing with the leading-edge block-matching methods and point-based methods, our method can produce far better detection results.