Provably Secure Robust Image Steganography
Zijin Yang, Kejiang Chen, Kai Zeng, Weiming Zhang, Nenghai Yu
Abstract
The maturity of generative models and the popularity of generated data have brought new technical means and camouflage environments to steganography. Numerous generative image steganography methods have emerged, but achieving provable security, robustness, and relatively high capacity simultaneously remains challenging. This paper proposes a provably secure robust image steganography method via the generative adversarial network (GAN), named PARIS. The sender maps the secret message, following a uniform distribution, to latent vectors conforming to a standard Gaussian distribution using inverse transform sampling. Subsequently, the latent vector is fed into the generator, producing the stego image. In this way, the stego image cannot be distinguished from the normally generated image. The receiver extracts the secret message from the recovered latent vector via gradient descent optimization. To enhance the robustness, a noise layer is introduced while recovering the latent vector to simulate potential lossy operations in real scenarios. The security of the proposed method is theoretically proven. Extensive experiments have also verified the proposed method's robustness, security, and relatively high capacity in terms of different GAN architectures, noises, and datasets.