Adaptive QIM With Minimum Embedding Cost for Robust Video Steganography on Social Networks

Sharing videos on social networks has become more and more popular, which provides a new scenario for covert communication. Video sharing-based hidden communication can conceal the contact relation between the sender and receiver and achieve the one-to-many delivery of secret messages. However, most video steganographic methods are unable to complete reliable hidden communication on social networks because of lossy video recompression. In this paper, we propose adaptive QIM (Quantization Index Modulation) with minimum embedding cost, which decreases the quantization distortion as much as possible to improve the security performance against steganalysis under the same level of robustness. Furthermore, based on the proposed quantization modulation scheme, we implement two robust steganographic methods in the DWT-SVD domain and the DTCWT-SVD domain. Experimental results show that the overall performance of our proposed modulation scheme outperforms QIM and adaptive QIM. Compared with existing robust video data hiding, two proposed steganographic methods demonstrate superior robustness and security on local lossy channels and social networks.