A VVC Video Steganography Based on Coding Units in Chroma Components with a Deep Learning Network

Abstract

Versatile Video Coding (VVC) is the latest video coding standard, but currently, most steganographic algorithms are based on High-Efficiency Video Coding (HEVC). The concept of symmetry is often adopted in deep neural networks. With the rapid rise of new multimedia, video steganography shows great research potential. This paper proposes a VVC steganographic algorithm based on Coding Units (CUs). Considering the novel techniques in VVC, the proposed steganography only uses chroma CUs to embed secret information. Based on modifying the partition modes of chroma CUs, we propose four different embedding levels to satisfy the different needs of visual quality, capacity and video bitrate. In order to reduce the bitrate of stego-videos and improve the distortion caused by modifying them, we propose a novel convolutional neural network (CNN) as an additional in-loop filter in the VVC codec to achieve better restoration. Furthermore, the proposed steganography algorithm based on chroma components has an advantage in resisting most of the video steganalysis algorithms, since few VVC steganalysis algorithms have been proposed thus far and most HEVC steganalysis algorithms are based on the luminance component. Experimental results show that the proposed VVC steganography algorithm achieves excellent performance on visual quality, bitrate cost and capacity.