A New High-Capacity Robust Image Steganography Method via Adversarial Network

Aiming at the problem that the high-capacity steganography model based on encoding-decoding network had weak robustness and could not resist noise attack and channel compression, an improved high-capacity robust image steganography algorithm based on adversarial network was proposed. In the proposed algorithm, encoder, decoder and critic based on densely connected convolutional networks (DenseNet) were designed. The secret information and the carrier image are jointly encoded into a steganographic image by the encoder, then the secret information was extracted by the decoder. The critic was used to discriminate between carrier images and steganographic images. A noise layer was added between encoder and decoder, Dropout, JPEG compression, gaussian blur, gaussian noise and salt and pepper noise were used to simulate a real environment of various kinds of noise attack. The steganography output by encoder was processed with different kinds of noise and decoded by decoder. Through model was trained, the message could be extracted from the noise-processed steganographic image by the decoder, so the ability to resist noise attacks was realized. The experiment results show that the steganographic capacity of the proposed algorithm reaches 0.450.95 bits per pixel (bpp) on $360 \times 360$ pixel images, which are 3.6 times higher than that of the earlier robust steganographic algorithms. The decoding accuracy reaches 0.72-0.97, which are 42% points higher than that of steganography without noise layer. The proposed algorithm not only guarantees high embedding quantity and high coding image quality, but also has stronger anti-noise capability.