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Channel Attention Image Steganography With Generative Adversarial Networks

Recently, extensive research has revealed the enormous potential of deep learning in the application of image steganography. However, some defects still exist in previous studies on deep learning-based steganography. In this paper, we propose a novel end-to-end network architecture for image steganography with channel attention mechanisms based on generative adversarial networks, which can yield perceptually indistinguishable stego images at various capacities. Three subnetworks constitute our model, where a generator embeds the payload into cover images, an extractor extracts it from stego images, and a powerful steganalyzer acts as a discriminator to enhance steganographic security. We design a specific channel attention module, which tunes channel-wise features in the deep representation of images dynamically by exploiting channel interdependencies. The experimental results demonstrate that the channel attention strategy is conducive to improving the quality of generated stego images and the accuracy of message extraction. To tackle the inevitable issue of extraction errors, we resort to error correction codes, with which our model achieves the maximum effective embedding rates over 4 bits per pixel. Finally, we verify that the proposed model outperforms current GAN-based steganographic schemes on two datasets and the undetectability is superior to traditional algorithms when the steganalyst cannot access model hyperparameters.