Previous research has demonstrated that a spatial domain image can provide side-information to its downsampled cover image, allowing a steganographer to embed a secret message on the cover image more securely by modulating the initial distortion. Importantly, the steganographer must possess the original image with a higher resolution than the cover image. In practical scenarios, however, the steganographer typically only has the cover image in which he wishes to embed the secret message; he does not have access to the real, higher-resolution image. To improve the security of steganography, we would like to estimate the side-information from the cover image. This paper proposes a spatial domain image steganography framework of side-information estimated with polarity adjustment strategy based on dual-path super-resolution reconstruction, in which double estimated side-information can be used to modulate the initial distortion. How to estimate more realistic high-resolution images and how to develop an effective modulation strategy are the central issues of our methods. We use double super-resolution networks to reconstruct high-resolution images for estimating side-information, and then propose a simple and effective strategy to modulate the initial distortion using dual-path estimated side-information. Experiments demonstrate that the security of dual-path side-information steganography can significantly outperform that of conventional distortion techniques.

Steganography is the technique of hiding secret messages within seemingly harmless covers to elude examination by censors. Despite having been proposed several decades ago, provably secure steganography has not gained popularity among researchers due to its rigorous data requirements. Recent advancements in generative models have enabled these researchers to provide explicit data distributions, which has contributed to the development of provably secure steganography methods. However, these methods depend on the assumption of a preshared key. In practical settings, these methods face various challenges, including key agreement, key updating, and user expansion. Although public-key steganography provides a viable solution, existing public-key steganography approaches are burdened with inefficiency and complex implementation in practical scenarios. In this paper, we proposes a practical public-key steganography method based on elliptic curve cryptography and a generative model. This method is the first comprehensive and practical approach to public-key steganography and steganographic key exchange. Additionally, we provide a specific instance to illustrate the proposed method. The security of the proposed construction is also proven based on computational complexity theory. Further experiments have demonstrated the security and efficiency of the proposed method.

Nowadays, social media has become the preferred communication platform for web users but brought security threats. Linguistic steganography hides secret data into text and sends it to the intended recipient to realize covert communication. Compared to edit-based linguistic steganography, generation-based approaches largely improve the payload capacity. However, existing methods can only generate stego text alone. Another common behavior in social media is sending semantically related image-text pairs. In this paper, we put forward a novel image captioning-based stegosystem, where the secret messages are embedded into the generated captions. Thus, the semantics of the stego text can be controlled and the secret data can be transmitted by sending semantically related image-text pairs. To balance the conflict between payload capacity and semantic preservation, we proposed a new sampling method called Two-Parameter Semantic Control Sampling to cutoff low-probability words. Experimental results have shown that our method can control diversity, payload capacity, security, and semantic accuracy at the same time.

Steganography is the act of disguising the transmission of secret information as seemingly innocent. Although provably secure steganography has been proposed for decades, it has not been mainstream in this field because its strict requirements (such as a perfect sampler and an explicit data distribution) are challenging to satisfy in traditional data environments. The popularity of deep generative models is gradually increasing and can provide an excellent opportunity to solve this problem. Several methods attempting to achieve provably secure steganography based on deep generative models have been proposed in recent years. However, they cannot achieve the expected security in practice due to unrealistic conditions, such as the balanced grouping of discrete elements and a perfect match between the message and channel distributions. In this paper, we propose a new provably secure steganography method in practice named Discop, which constructs several "distribution copies" during the generation process. At each time step of generation, the message determines from which "distribution copy" to sample. As long as the receiver agrees on some shared information with the sender, he can extract the message without error. To further improve the embedding rate, we recursively construct more "distribution copies" by creating Huffman trees. We prove that Discop can strictly maintain the original distribution so that the adversary cannot perform better than random guessing. Moreover, we conduct experiments on multiple generation tasks for diverse digital media, and the results show that Discop’s security and efficiency outperform those of previous methods.