  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.
