Double Compression Detection Based on the De-Blocking Filtering of HEVC Videos

Abstract

Instead of detecting whether the whole video sequence is double compressed, a frame-level detection result can provide more precise information for video forensic tasks, such as locate tamper point and restore compression history, et al. But the research on frame-level double compression detection is still in its infancy. Therefore we aim to provide a frame-level detection method for HEVC videos in this paper. The relocated I(RI) frame belongs to different GOP groups from its reference frame at the first compression and may cause more severe blocking effects than other types of P frames. Hence, this paper proposes an algorithm based on the de-blocking filtering feature mode to detect RI frames in the double compressed HEVC videos with shifted GOP structure. Firstly, the abnormal traces of the de-blocking filtering parameters, such as boundary strength, filtering switch and filtering mode, in the RI frame are analyzed. Then, the de-blocking filtering feature is constructed by mapping the different combinations of the three parameters into a single numerical value. Finally, the de-blocking filtering feature of the video clips is adopted as the input of the proposed mini_MobileViT network, which is the combination of Convolutional Neural Network (CN-N) and Transformer, to learn spatial and temporal representations to identify the RI frames. Experimental results demonstrate the advantages of the proposed algorithm in detecting RI frames in the double compressed HEVC videos. Compared with the state-of-art work He’s method, the proposed method has a 1.72% improvement in the accuracy of detecting RI frames. Compared with other traditional methods, there is a more than 10% improvement.