A robust copy-move forgery detection technique based on discrete cosine transform and cellular automata

Abstract

Copy Move Forgery (CMF) is a type of digital image forgery in which an image region is copied and pasted to another location within the same image with malicious intent to misrepresent its meaning. To prevent misinterpretation of an image content, several Copy Move Forgery Detection (CMFD) methods have been proposed in the past. However, the existing methods show limited robustness on images altered with post-processing attacks such as noise addition, compression, blurring etc. In this paper, we propose a robust method for detecting copy-move forgeries under different post-processing attacks. We use Discrete Cosine Transform (DCT) to extract features from each block. Next, Cellular Automata is employed to construct feature vectors based on the sign information of the DCT coefficients. Finally, feature vectors are matched using the kd-tree based nearest-neighbor searching method to find the duplicated areas in the image. Experimental results show that the proposed method performs exceptionally well relative to the other state-of-the-art methods from the literature even when an image is heavily affected by the post-processing attacks, in particular, JPEG compression and additive white Gaussian noise. Furthermore, experiments confirm the robustness of the proposed method against the range of combined attacks.