Real image noise aware steganography with image denoising and generative adversarial network

Abstract

Image steganography is a technique for embedding secret messages in images. SteganoGAN, one of the previous methods, uses generative adversarial networks and achieves high payload when the input is a noise-free image. However, real images contain real image noise (RIN) generated during the image acquisition process, which can degrade the performance of SteganoGAN. We propose a RIN-aware image steganography that uses a real image denoising method as preprocessing and modifies the loss function of SteganoGAN. These modifications encourage the proposed method to embed a secret message that simulates RIN into a pseudo-noise-free image obtained by denoising. Experimental results show that the proposed method improves the trade-off between image quality and payload compared to the previous method. We validate the statistical and neural steganalysis and JPEG robustness, showing that the proposed method has reasonable detection avoidance capability and higher compression tolerance than the conventional methods.