Heard More Than Heard: An Audio Steganography Method Based on GAN

Audio steganography is a collection of techniques for concealing the existence of information by embedding it within a non-secret audio, which is referred to as carrier. Distinct from cryptography, the steganography put emphasis on the hiding of the secret existence. The existing audio steganography methods mainly depend on human handcraft, while we proposed an audio steganography algorithm which automatically generated from adversarial training. The method consists of three neural networks: encoder which embeds the secret message in the carrier, decoder which extracts the message, and discriminator which determine the carriers contain secret messages. All the networks are simultaneously trained to create embedding, extracting and discriminating process. The system is trained with different training settings on two datasets. Competed the majority of audio steganographic schemes, the proposed scheme could produce high fidelity steganographic audio which contains secret audio. Besides, the additional experiments verify the robustness and security of our algorithm.