Multiple forgeries identification in digital video based on correlation consistency between entropy coded frames

A tremendous amount of video data is transferred over the Internet from one location to another, and its amount is growing exponentially every day. Significant advances made in multipurpose video editing software technology have considerably increased the chances of digital video tampering/forgeries. Therefore, the authenticity of digital video has become important. In this paper, the passive algorithm based on the correlation consistency between entropy coded (DistrEn2D and MSE2D) frames for video forgery detection is proposed. The entropy-based texture feature, such as two-dimensional distribution entropy (DistrEn2D) and bi-dimensional multiscale entropy (MSE2D), is used in the proposed algorithm. This algorithm works in four stages and can investigate the presence of multiple forgeries in the videos. The first stage is pre-processing. In step second, the texture feature is extracted from the video frames. After that, inter-frame correlation consistency between Entropy coded frames is investigated to detect multiple forgeries. In the final stage, multiple forgeries are localized in the video using an abnormal point detection. An experimental result shows that the proposed algorithm (using DistrEn2D and MSE2D feature) provides better performance in identifying the forgeries present in the video.