One-Class Double Compression Detection of Advanced Videos Based on Simple Gaussian Distribution Model

• Published in: IEEE transactions on circuits and systems for video technology Vol. 32; no. 4; pp. 2496 - 2500
• Main Authors: Li, Qiushi, Chen, Shengda, Tan, Shunquan, Li, Bin, Huang, Jiwu
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Anomalies; Cameras; Classification; Classifiers; Compressing; Discrete cosine transforms; Double compression detection; ensemble strategy; Feature extraction; Forensic science; Frames (data processing); Image coding; Labels; Normal distribution; one-class classification; SPAM; Supervised learning; Surveillance; Training; Video compression; Video data; video forensics; Videos
• ISSN: 1051-8215; 1558-2205
• DOI: 10.1109/TCSVT.2021.3069254

Passive video forensics has become an active topic in recent years. Generally, a pristine video obtained from surveillance cameras or other video recording devices is single compressed. At the same time, double lossy compression will certainly be introduced in tampered videos since it needs to go through recompression to perform tampering on a commonly used compressed video. In the video's double compression, some traces are left due to the intrinsic effects of recompression. Traditional supervised learning is inefficient because it requires two classes (the pristine and the manipulated) that occur in the video to be exhaustively assigned labels. Actually, compared with pristine videos, manipulated videos with labels are more difficult to obtain. To address this problem, in this paper, one-class classification, which is often used for anomaly detection and only needs the target class, is introduced. We first treat all decompressed video frames as still images and extract subtractive pixel adjacency matrix (SPAM) steganalysis features to detect traces left in the double compression process. Then we adopt a Gaussian density-based one-class classifier since SPAM features extracted from pristine video frames approximately subject to the Gaussian distribution. Furthermore, we improve the robustness of the classifier by using ensemble strategy. Experimental results indicate that our proposed method exceeds other more complex one-class classification methods, and outperforms fully-supervised learning methods only by feeding features from single compressed video frames to our one-class classifier.