The cover image with strong resistance against embedding distortion has promise for improving the holistic security of steganography. However, existing methods use the vulnerability of Convolutional Neural Network (CNN) to construct the enhanced cover which can only deceive the target CNN-based steganalyzer, but is not more suitable for steganography. When resisting steganalysis outside the target steganalyzer, its performance drops significantly. In this paper, we propose an immune-cover construction scheme via Artificial Immune System (AIS). By the association between the steganography and immune theory, we regard the cover as the organism, the distortion introduced by steganography as the pathogenic factor, and the immunoprocessing for optimizing the original cover as the antibody. Based on AIS, the optimal immunoprocessing is dynamically searched and performed on the original cover to construct an immune-cover which is most suitable for steganography. Besides, the proposed method carefully selects the immunoprocessing region to prevent artifacts, and guarantees the visual quality of the immune-cover through the constraint of the immunoprocessing intensity. Extensive experimental results demonstrate that the proposed immune-cover has much stronger resistance against embedding distortion compared with the related methods, thus significantly improving the holistic security of the adaptive steganography evaluated on both traditional and CNN-based steganalyzers.

Recent advancements in image steganography demonstrate that reasonable cover enhancement approaches can effectively improve the security performance of steganography. However, the existing proposals based on adversarial steganography against targeted steganalyzers are insufficient in terms of eliminating or reducing anomalous pixels caused by subsequent embedding. This limitation impedes the full potential of cover enhancement schemes for enhancing steganographic security. In this article, we present Immucover, a novel immunized cover image construction method that leverages fuzzy enhancement and an artificial immune system (AIS) to incorporate texture region- and edge-region-adaptive enhancement. Specifically, we first design a parameterized method to adaptively enhance the texture region of the given cover image using a distortion function. Then, Immucover detects and enhances the edge region of the cover image using a fuzzy-parameterized approach based on an optimized smallest univalue segment assimilating nucleus for edge detection. Finally, a powerful AIS module acts as an optimizer to optimize the parameters that affect the texture and edge area, i.e., the area where the secret information is suitably embedded. In this way, a so-called immunized cover image is generated. In addition, we develop a novel affinity metric to assess the antibody quality within the AIS module, which guides the generation of the immunized cover with higher security. Comprehensive experiments conducted on widely used datasets demonstrate that our Immucover provides significantly improved resistance to steganalysis and enhances the security of steganography.

Performing post-processing on the stego image has promise for improving the steganography security. Nevertheless, the existing post-processing schemes neglect the characteristics of the stego image, which lack strong theoretical interpretability. Moreover, existing schemes do not fully consider the holistic steganography security against both CNN-based and handcrafted steganalyzers. In this paper, we propose a steganography immunoprocessing (IP) framework based on Artificial Immune System (AIS) that is universal for the stego images from the same steganographic process to further enhance the security. Based on the natural relationship between immune theory and steganography, we regard the immunoprocessing policy as the antibody, and the performance of the anti-steganalysis for stego images protected by antibody as the antibody affinity. By the immune dynamic optimization process, the optimal immunoprocessing policy is dynamically searched and performed on the stego image to achieve further optimization. In addition, we enhance the resistance of the stego against the target CNN-based steganalyzer by limiting the immunoprocessing direction. Performing the optimal immunoprocessing on stego images will enhance the holistic security of steganography. Experimental results demonstrate that the proposed immunoprocessing can significantly improve the holistic security of adaptive steganography against both CNN-based and handcrafted steganalyzers, and achieve better performance than related schemes.

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.