Aiming at the problem of low embedding capacity and poor invisibility in compressed domain video hiding algorithm, a reversible steganography scheme for H.264/AVC encryption domain was proposed. Firstly, the reference frame interval parameter was determined by the embedded capacity and the carrier size, and whether the cover was encrypted was determined by the need. Then, an embedded key was generated according to the number of video frames to be embedded. Finally, the reversible information embedding on motion vector was realized by the vector histogram shifting in the compressed video. The proposed scheme overcame the distortion accumulation effect due to motion vector modification by specifying a decoding reference frame and is compatible with motion vector-based video encryption algorithms. Video decryption and information extraction depend on the decryption key and the embedded key respectively, which are separated from each other. The information can be extracted in the video ciphertext domain or the decrypted plaintext domain and has no influence on video cover recovery. As security of the information depends on the embedded key, the length of the key can be controlled as needed with the maximum length equal to the number of frames in which the information can be embedded. Experimental results show that the proposed scheme has low computational complexity and high security, and can adjust capacity and invisibility according to embedded load. Compared with BCH code reversible embedding scheme, the PSNR (Peak Signal-to-Noise Ratio) value increases by 3 to 5 dB and the average embedded capacity increases by 5 to 10 times.

Focusing on the issue that information-hidden carriers will leave traces of modification, and it is fundamentally difficult to resist statistical steganalysis algorithm detection, a new security steganography model based on Generative Adversarial Network (GAN) was proposed. In this scheme, the generator model G in GAN was utilized to generate the original carrier information with noise as the driver. Next, by using the ±1 embedding algorithm, the secret message was embedded into the generated carrier information to generate the secret information. Finally, the secret information and the real image sample were used as the input of discriminator D in the GAN for iterative optimization. At the same time, discriminative model S was used to detect whether the image has a steganography operation, and timely feedback to generate image quality features, G&D&S competed with each other in the iterative process, and the performance was continuously improved. The proposed strategy is different from the two schemes of Steganographic GAN (SGAN) and Secure Steganography based on GAN (SSGAN). The main feature is that the secret information and the real image sample are used as input for the discriminative model, and the discriminative network D is reconstructed, so that the network can better evaluate the performance of the generated images. Compared with SGAN and SSGAN, the proposed model reduces the detection accuracy of steganalysis by 13.1% and 6.4% respectively. Experimental results show that the new information hiding scheme guarantees the security of information hiding by generating more suitable carrier information and can effectively resist the detection of steganographic algorithms, it is significantly superior to the contrast schemes in terms of anti-steganography analysis and security indicators.

To solve the problem that some video steganography algorithms based on Motion Vector (MV) change statistical features of histogram, a new video steganography algorithm based on keeping histogram of MV was proposed. In this paper, the secret information were hidden in video MV by using the data mapping of histogram. At the same time, a code-matching method was used to encode the secret information before being embedded, then the data stream would have the same statistical characteristics by MV, which made the scheme absolutely secure in theory. The experimental results show that: the change of histogram features is effectively controlled, the increment of bitrate is controlled in 1%, and the steganalysis detection rate is decreased by an average of 30% to 50%.