Generative Steganography via Auto-Generation of Semantic Object Contours

Abstract

As a promising technique of resisting steganalysis detection, generative steganography usually generates a new image driven by secret information as the stego-image. However, it generally encodes secret information as entangled features in a non-distribution-preserving manner for the stego-image generation, which leads to two common issues: 1) limited accuracy of information extraction, and 2) low security in feature-domain. To address the above issues, we propose a generative steganographic framework via auto-generation of semantic object contours, in which a given secret message is encoded as the disentangled features, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., object-contours, in a distribution-preserving manner for the stego-image generation. In this framework, we propose a contour generative adversarial nets (CtrGAN) consisting of a contour-generator and a contour-discriminator, which are adversarially trained with reinforcement learning. To realize the generative steganography, by using the contour-generator of the trained CtrGAN, a contour point selection (CPS)-based encoding strategy is designed to encode the secret message as the contours. Then, the BicycleGAN is employed to transform the generated contours to the corresponding stego-image. Extensive experiments demonstrate the proposed steganographic approach achieves superior performance in the aspects of information extraction accuracy, especially under common image attacks, and feature-domain security, compared to the state-of-the-arts.