# An End-to-End Video Steganography Network Based on a Coding Unit Mask
## 1 Introduction  
Seeing is not always trustworthy. That is to say, a normal-looking video frame may contain secret information that is invisible to users. Information hiding enables people to conceal secret information within any kind of media. The internet has revolutionized global development in the sense that it has become the most convenient and economical telecommunication medium. Early internet communication was mainly in the form of text. However, over the last two decades, images and videos have gradually become the most popular form of multimedia communication. Different from cryptography, steganography pursues imperceptibility, which requires hiding information without arousing people's perception [1, 2]. Steganography is applied wherever secret communication is required [3]. Its application fields include military, medical, and multimedia fields, where steganography has been established as a promising and competitive method. For example, the famous \"9/11\" incident was caused by terrorists who escaped the monitoring of U.S. intelligence agencies by using digital image steganography technology, and who finally succeeded in triggering an attack. On an individual level, when works are illegally identified or copied, robust steganography can contribute to identifying users and protecting copyright [4, 5]. Traditional steganography requires expertise in the hiding process [6], where a well-defined cost function is crucial because it is a comprise between embedding distortion andpayload capacity [7; 8; 9; 10; 11; 12]. Traditional steganography has many years of research history, and many excellent methods have achieved good results in the visual quality and hidden capacity of steganographic images, in which the authors of [12] used the concept of pixel value differencing and modulus function (PVDMF) to achieve an excellent performance. However, due to its rapid development and powerful representational capabilities, deep learning is widely used in many industries, including autonomous driving, automatic translation, speech recognition, etc. Deep-learning-based steganography is adaptable and generalized, which saves a lot of artificial workloads. It does not need to manually design features and the convolutional neural network (CNN) can adaptively select features for information hiding. Correspondingly, it does not need to manually extract information and CNN can extract information conveniently and quickly. There are already many deep learning-based steganography algorithms. The steganography model proposed in [13] can resist various lossy image and video compression, including non-differentiable JPEG compression. SSGAN [14] utilizes a new form of generative adversarial network (GAN) to resist steganalysis. SGAN [15] is a secure model for generating image-like containers based on deep convolutional generative adversarial networks (DCGAN). Among deep-learning-based hiding methods [16; 17; 18; 19; 20; 21; 22], end-to-end steganography is a completely different and groundbreaking method, which is a three-player game [19; 20; 21; 22] based on successful generative adversarial network (GAN) [23; 24] that contains an encoder, a decoder, and an adversary. It is a new type of data hiding, which trains the embedding and extracting process simultaneously, and is designed to specifically work as a pair. In the family of three-player games, HiDDeN [21] and SteganoGAN [22] adopt the encoder structure and decoder structure to perform information embedding and information recovery processes, respectively. Furthermore, a third adversary network that plays a role in steganalysis is utilized. However, whether traditional steganography methods [25; 26] or the deep-learning-based steganography, only a few of them take digital videos as carriers. Only two references refer to end-to-end video steganography. RivaGAN processes 3D video sequences using a 3D convolution kernel [27].