Using similarity analysis to detect frame duplication forgery in videos

Duplication of selected frames from a video to another location in the same video is one of the most common methods of video forgery. However, few algorithms have been suggested for detecting this tampering operation. This paper proposes an effective similarity-analysis-based method for frame duplication detection that is implemented in two stages. In the first stage, the features of each frame are obtained via SVD (Singular Value Decomposition). Next, the Euclidean distance is calculated between features of each frame and the reference frame. After dividing the video sequence into overlapping sub-sequences, the similarities between the sub-sequences are calculated, and then our algorithm identifies those video sequences with high similarity as candidate duplications. In the second stage, the candidate duplications are confirmed through random block matching. The experimental results show that our algorithm provides detection accuracy that is higher than the previous algorithms, and it has an outstanding performance in terms of time efficiency.