Detection of Double-Compressed Videos Using Descriptors of Video Encoders

Sensors (Basel). 2022 Nov 29;22(23):9291. doi: 10.3390/s22239291.

Abstract

In digital forensics, video becomes important evidence in an accident or a crime. However, video editing programs are easily available in the market, and even non-experts can delete or modify a section of an evidence video that contains adverse evidence. The tampered video is compressed again and stored. Therefore, detecting a double-compressed video is one of the important methods in the field of digital video tampering detection. In this paper, we present a new approach to detecting a double-compressed video using the proposed descriptors of video encoders. The implementation of real-time video encoders is so complex that manufacturers should develop hardware video encoders considering a trade-off between complexity and performance. According to our observation, hardware video encoders practically do not use all possible encoding modes defined in the video coding standard but only a subset of the encoding modes. The proposed method defines this subset of encoding modes as the descriptor of the video encoder. If a video is double-compressed, the descriptor of the double-compressed video is changed to the descriptor of the video encoder used for double-compression. Therefore, the proposed method detects the double-compressed video by checking whether the descriptor of the test video is changed or not. In our experiments, we show descriptors of various H.264 and High-Efficiency Video Coding (HEVC) video encoders and demonstrate that our proposed method successfully detects double-compressed videos in most cases.

Keywords: digital forensics; double compression detection; video forgery detection; video tampering detection.

MeSH terms

  • Data Compression* / methods

Grants and funding

The present research has been conducted by the Research Grant of Kwangwoon University in 2020. This work was supported by the Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No. 2020-0-00011).