Adaptive and High-security Image Steganography via Adversarial Embedding

The existing adversarial embedding methods, which based on distortion cost function and syndrome trellis codes STCs , achieve adversarial embedding by manually adjusting the embedding cost. To address the limitation of hand-crafted embedding cost, we propose an end-to-end adversarial image steganography method, which automatically achieves adversarial embedding by using the gradients from the steganalytic network. We utilize gradients of the stego image to generate the adversarial embedding mask, then integrate it wtih the loss function to guide the secret messages embedded into the specific security-enhanced regions. Comparing with several state-of-the-art steganography methods, extensive experimental results demonstrate that our method significantly improves the security performance against convolutional neural network CNN -based steganalyzers and re-trained steganalyzers. For example, when against steganalyzers, the security improvement in terms of detection accuracy of our method achieves 30.68 higher than the SOTA steganography methods at 0.4 bpp bit per pixel .