Generative steganography method based on auto-generation of contours

Abstract

To address the problems of limited hiding capacity and inaccurate information extraction in the existing generative steganography methods, a novel generative steganography method was proposed based on auto-generation of contours, which consisted of two main stages, such as the contour generation driven by secret information and the contour-to-image transformation.Firstly, the contour generation model was built based on long short term memory (LSTM) for secret information-driven auto-generation of object contours.Then, a contour-to-image reversible transformation model was constructed based on pix2pix network to obtain the stego-image, and the model also supported the reversible transformations from the stego-image to contours for secret information extraction.Experimental results demonstrate that the proposed method not only achieves high hiding capacity and accurate information extraction simultaneously, but also effectively resists the attacks by steganalysis tools.It performs much better than the state-of-the-art generative steganographic methods.

Keywords

• generative steganography;coverless information hiding;deep learning;generative adversarial network