Frame duplication and shuffling forgery detection technique in surveillance videos based on temporal average and gray level co-occurrence matrix

Nowadays, due to the increasing crime and theft around the world, surveillance security systems play an important role. On the other hand, the availability of video editing tools has made authenticity of video contents significant and urgent mission to use as strong evidence in the courts. Frame duplication with/without shuffling is a common form of video forgery to repeat or cover-up an event in a video's scene. In this paper, we propose a robust method to detect inter-frame duplication forgery using a temporal average of each shot and statistical textural features. Duplicated shots containing frames that are reordered during the forgery process (frame shuffling), cannot be classified as tampered shots by the existing methods leading to an increase in false positives. To address this issue, we use a temporal average of each shot which found to be invariant with different orders. Our method is capable of detecting duplicate shots that do not have any tracing points (discontinuity points). Experimental results show that our method has achieved improved accuracy on frame duplication detection with lower computational time. Furthermore, it has successfully detected frame shuffling with high accuracy rates, even when the forged video has undergone post-processing operations such as Gaussian blurring, noise addition, brightness modification, and compression.