Abstract:

Compared with the conventional methods of video forgeries, such as frame insertion,frame deletion and double compression, copy-move forgery might change the content of a video directly. Therefore, a new algorithm was proposed to detect video copy-move forgery by fusing multiple features. For an intraframe copy-move tampered video, which may introduce a number of sharp transitions in frames such as lines, edges and corners. Phase congruency was known as a sensitive measure of these sharp transitions and hence was proposed as features for video forgery detection. Meanwhile, the duplicated blocks from the heterologous video could introduce different pattern noise. Therefore, the proposed algorithm could extract features of video frames from the pattern noise and 2-D phase congruency for video forgery detection, and then the merged multiple features were experimented with the support vector machine (SVM). Experimental results demonstrated that the proposed algorithm could detect video of intra-frame copy-move forgery effectively.

Key words: copy-move detection, support vector machine, pattern noise, copy-move forgery, 2-D phase congruency