Dig a Hole and Fill in Sand: Adversary and Hiding Decoupled Steganography

Deep steganography is a technique that imperceptibly hides secret information into image by neural networks. Existing networks consist of two components, including a hiding component for information hiding and an adversary component for countering against steganalyzers. However, these two components are two ends of the seesaw, and it is difficult to balance the tradeoff between message extraction accuracy and security performance by joint optimization. To address the issues, this paper proposes a steganographic method called AHDeS (Adversary-Hiding-Decoupled Steganography) under the Dig-and-Fill paradigm, wherein the adversary and hiding components can be decoupled into an optimization-based adversary module in the digging process and an INN-based hiding network in the filling process. Specfically in the training stage, the INN is first trained for acquiring the ability of message embedding. In the deployment stage, given the well-trained and fixed INN, the cover image is first iteratively optimized for enhancing the security performance against steganalyzers, followed by the actual message embedding by the INN. Owing to the reversibility of the INN, security performance can be enhanced without sacrificing message extraction accuracy. Experimental results show that AHDeS can achieve the state-of-the-art security performance and visual quality while maintaining satisfied message extraction accuracy.