Affiliations: [a] School of Intelligence Artificial, Hebei University of Technology, Tianjin, China
| [b] College of Computer Science and Technology, Jilin University, Changchun, China
Correspondence:
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Corresponding author. Xuanjing Shen, College of Computer Science and Technology, Jilin University, Changchun, China. E-mail: xjshen@jlu.edu.cn.
Abstract: To abandon the use of overlapping block division and resist the tampering factor of illumination change, a novel copy-move forgery detection method is proposed based on Maximally Stable Extremal Regions (MSERs) and the Local Intensity Order Pattern (LIOP), that integrates block-based and keypoints-based methods. The method involves the following steps: first, affine transformation invariant MSERs are used to maintain the geometrical transformation invariance and reduce computational complexity; second, LIOP features are used to describe the texture and resist illumination change; and finally, RANdom SAmple Consensus is applied to remove false matches. The experiments indicate that the proposed method has great performance for scaling, rotation and illumination changes. Moreover, the method has the high robustness to Gaussian noise, Gaussian blur and JPEG compression.
Keywords: Image forensics, copy-move forgery detection, maximally stable extremal regions, local intensity order pattern
