Video traffic has become the principal part of data resources in the current cyberspace which brings many challenges such as security, stability and scalability of streaming transmission. Moreover, how to ensure high visual quality while obtaining a significant bit-rate reduction has always been the focus of the industry. By constructing a source distortion temporal propagation (SDTP) model, this paper proposes a temporal dependent RDO (TDRDO) algorithm to resolve the global RDO problem in the temporal domain. Besides, a fuzzy logic based rate control (FLRC) algorithm is proposed to robustly regulate encoding bit-rates. The two algorithms have previously been adopted by Audio Video Coding Standard Workgroup of China and integrated into the second generation (AVS2). Experimental results prove the excellence of the proposed algorithms, for significantly improving the AVS2 video coding performance and providing AVS2 with superb efficiency to compete with HEVC/H.265 in modern video compression.

Recently, adversarial perturbations have been used to reassign cost which can enhance the security of steganography, called as adversarial embedding. However, existing methods selected costs to be modified by self-defined rules which were hard to achieve the optimal security against steganalyzers. In this paper, we propose an automatic adversarial embedding scheme called RLAE (deep Reinforcement Learning-based content-adaptive Adversarial Embedding). In RLAE, an agent network utilizes a generative network which generates an embedding policy for cost reassignment automatically according to a basic steganography cost map. Then, an environment network employs a steganalyzer as an attack target that offers rewards for optimizing the agent network. To provide more comprehensive information, we design a joint reward by considering both the adversarial perturbations calculated from the environment network and noise residual signal representing image textures. Experimental results show that the security of the proposed RLAE is superior than state-of-the-art works, especially steganography with for the large payloads.

Adaptive image steganography is the process of embedding secret messages into undetectable regions of a cover image through the design of a distortion function by a steganographer. Since the state-of-the-art steganalyzers are mainly based on image residual analysis, it is reasonable to modify stego image for withstanding steganalysis by reducing or eliminating the image residual distance between cover and stego image. However, simply modifying stego images may lead to message extraction failure and the introduction of additional detectable artifacts. In this paper, we propose a novel secure steganography strategy by constructing immunized stego-image via an artificial immune system, called ISteg, which ensures the accurate extraction of hidden data while enhancing the security against steganalyzers. Inspired by the biological immune system, we use an artificial immune system (AIS) to build ISteg. Specifically, ISteg generates the immunized stego-image by automatically modifying the stego to maximize the affinity of the antibody. The affinity is developed to evaluate antibody quality according to the Euclidean distance between the residual co-occurrence matrix features of the cover image and the modified stego image. In this manner, the so-called immunized stego-image is generated. Extensive experimental results demonstrate that the proposed ISteg strategy can effectively improve the security performance of existing steganography.

The cost function is crucial to the security of adaptive image steganography, However, some existing cost functions are heuristically designed and hard to be optimal in the undetectability against the evolving steganalyzer. In this letter, we propose a cost reassignment algorithm for adaptive steganography based on artificial immune system. Under the scenario of minimizing additive distortion, this method reassigns the cost by adjusting the modification probability distribution obtained by the cost function, and dynamically optimizes the adjustment mode through the immunity-based information hiding model, so that the modified pixels are more concentrated in the regions that are difficult to be detected. The experimental results show that the proposed method is suitable for a variety of the state-of-the-art cost functions and can achieve better performance on resisting the steganalysis.