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 a practical robust invertible network for image steganography (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 approximate function (GAF) is also proposed to overcome the undifferentiable issue of rounding distortion. Experimental results show that our method outperforms the state-of-the-art robust image steganography method in both robustness and practicability, achieved an average Peak Signal to Noise Ratio value of 34.28/32.96 on container/secret pairs under 5 different attacks. Codes are available at https://github.com/yanghangAI/PRIS, demonstration of our model in practical at http://yanghang.site/hide/.