DKiS: Decay weight invertible image steganography with private key

Image steganography, the practice of concealing information within another image, traditionally faces security challenges when its methods become publicly known. To counteract this, we introduce a novel private key-based image steganography technique. This approach ensures the security of hidden information, requiring a corresponding private key for access, irrespective of the public knowledge of the steganography method. We present experimental evidence demonstrating our method's effectiveness, showcasing its real-world applicability. Additionally, we identified a critical challenge in the invertible image steganography process: the transfer of non-essential, or `garbage', information from the secret to the host pipeline. To address this, we introduced the decay weight to control the information transfer, filtering out irrelevant data and enhancing the performance of image steganography. Our code is publicly accessible at https://github.com/yanghangAI/DKiS, and a practical demonstration is available at http://yanghang.site/hidekey.

PRIS: Practical robust invertible network for image steganography

Image steganography is a technique of hiding secret information inside another image, so that the secret is not visible to human eyes and can be recovered when needed. Most of the existing image steganography methods have low hiding robustness when the container images affected by distortion. Such as Gaussian noise and lossy compression. This paper proposed PRIS to improve the robustness of image steganography, it based on invertible neural networks, and put two enhance modules before and after the extraction process with a 3-step training strategy. Moreover, rounding error is considered which is always ignored by existing methods, but actually it is unavoidable in practical. A gradient approximation function (GAF) is also proposed to overcome the undifferentiable issue of rounding distortion. Experimental results show that our PRIS outperforms the state-of-the-art robust image steganography method in both robustness and practicability. Codes are available at https://github.com/yanghangAI/PRIS, demonstration of our model in practical at http://yanghang.site/hide/.