CamStegNet: A Robust Image Steganography Method Based on Camouflage Model

Deep learning models are increasingly being employed in steganographic schemes for the embedding and extraction of secret information. However, steganographic models themselves are also at risk of detection and attacks. Although there are approaches proposed to hide deep learning models, making these models difficult to detect while achieving high-quality image steganography performance remains a challenging task. In this work, a robust image steganography method based on a camouflage model CamStegNet is proposed. The steganographic model is camouflaged as a routine deep learning model to significantly enhance its concealment. A sparse weight-filling paradigm is designed to enable the model to be flexibly switched among three modes by utilizing different keys: routine machine learning task, secret embedding task and secret recovery task. Furthermore, a residual state-space module and a neighborhood attention mechanism are constructed to improve the performance of image steganography. Experiments conducted on the DIV2K, ImageNet and COCO datasets demonstrate that the stego images generated by CamStegNet are superior to existing methods in terms of visual quality. They also exhibit enhanced resistance to steganalysis and maintain over 95% robustness against noise and scale attacks. Additionally, the model demonstrates high robustness which can achieve excellent performance in machine learning tasks and maintain stability across various weight initialization methods.