A Robust Coverless Steganography Based on Generative Adversarial Networks and Gradient Descent Approximation
Fei Peng, Guan-Fu Chen, Min Long
Abstract
Aiming at resolving the problem of the irreversibility in some common neural networks for secret data extraction, a novel image steganography framework is proposed based on the generator of GAN (Generative Adversarial Networks) and gradient descent approximation. During data embedding, the secret data is first mapped into a stego noise vector by a specific mapping rule, and it is input into the generator of a GAN to produce a stego image. The data extraction is accomplished by iteratively updating the noise vector using the gradient descent with the generator. When the error is declined within the allowable error, the output image of the generator is approximate to the stego image, and the updated noise vector will also approach to the stego noise vector. Finally, the secret data is extracted from the updated noise vector. Experiments and analysis with WGAN-GP (Wasserstein GAN-Gradient Penalty) show that it can achieve good performance in extraction accuracy, capacity and robustness. Furthermore, the discussions also illustrate its good generalization with different GAN models and image datasets.