A Robust Coverless Video Steganography Based on Two-Level DCT Features Against Video Attacks

Laijin Meng, Xinghao Jiang, Qiang Xu, Tanfeng Sun

Abstract: Compared with traditional video steganography, coverless video steganography (CVS) can completely avoid being detected by steganalysis algorithms. Recently, the study of CVS has developed rapidly. However, it is still far from the theoretical maximum values in capacity, i.e., the theoretical limit is $2^\ell$ for a hash sequence length of $\ell$. Besides, most existing CVS methods have only considered limited types of video attacks in robustness. In this paper, a novel coverless video steganography based on two-level discrete cosine transform (DCT) features is proposed. First, pre-processing is accomplished on the public video datasets. Then, two-level DCT features are calculated and the Coverless Video Database (CVD) is constructed by the K-means++ clustering algorithm. After that, the mapping table is established to map the secret segments to the CVD. Finally, each secret segment corresponds to a video sequence in the CVD by the mapping table to complete the process of information embedding and extraction. The proposed method first evaluates the robustness against the frame swapping attack, which is a common video attack. Experimental results show that the proposed method can achieve the theoretical maximum value in effective capacity and better robustness compared to the state-of-the-art works.

External IDs:doi:10.1109/tmm.2025.3586104