Universal Detection of Video Steganography in Multiple Domains Based on the Consistency of Motion Vectors
Liming Zhai, Lina Wang, Yanzhen Ren · IEEE Transactions on Information Forensics and Security · 2019
Digital video provides various types of embedding domains, which lead to a great diversity in video steganography. However, in the detection of video steganography, the existing video steganalytic features all specialize in a particular domain, and are hardly to detect the steganography in other embedding domains. In this paper, we propose a universal feature set which is capable of detecting the video steganography in multiple domains. Two popular embedding domains, i.e., partition mode (PM) domain and motion vector (MV) domain, are considered for steganalysis. The idea is based on the observation that the MVs of the sub-blocks in the same macroblock are usually different from each other, and they will tend to be consistent in values after the MV modifications or PM modifications. Thus the consistency of MVs can be used as an evidence for the steganographic embedding in two domains, and finally a 12-dimensional feature set is designed for universal detection. Extensive experiments are conducted to demonstrate the effectiveness of the proposed feature set. The results show that our feature set achieves superior universality and accuracy in both PM domain and MV domain, and even performs well in mismatched domains, where the detection model trained in one domain can directly be used to attack the steganography in another domain. Besides, the low complexity of the proposed feature set also indicates its advantage in real-time video steganalysis.