PRIS: Practical robust invertible network for image steganography
This work addresses robustness issues in image steganography for secure data hiding, representing an incremental improvement with novel modules and training strategies.
The paper tackled the problem of low robustness in image steganography when container images are distorted by noise or compression, and proposed PRIS, an invertible neural network-based method that outperformed state-of-the-art methods in robustness and practicability.
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/.