Detection of frame deletion in HEVC-Coded video in the compressed domain

In this paper, we propose an algorithm for detecting frame deletion in HEVC-coded video in the compressed domain. Specifically, we focus on the frame type changes occurring upon frame deletion, which cause slight differences between the coding patterns in original and forged video. Then, we identify discriminating coding patterns for use as features, which are classified by machine learning classifiers. Furthermore, we propose video sequence integrity detection on a group-of-picture basis, which is computationally efficient and robust even when applied to static videos and videos with genuine scene changes. The experimental results show that the proposed technique can classify HEVC-coded videos more accurately than previous methods. In addition, the results demonstrate that the selected features work harmoniously in discrimination and that the learning-based classifiers are more robust and reliable than model-based classifiers.