SmartSteganogaphy: Light-weight generative audio steganography model for smart embedding application

Massive data is transferred in the Internet of Thing (IoT) every single second. Protecting the security of these data is a crucial task. Steganography is a collection of techniques for concealing the existence of information by embedding it within irrelevant carriers, which could protect security and privacy of the data. Distinct from the cryptography, steganography put emphasis on hiding the existence of the secret. However, the majority of research mainly work on complex algorithm. In this paper, we proposed an audio steganography algorithm which automatically generated from adversarial training. The embedding model is light-weight which could use as machine learning tools in smart device. Besides, the existing audio steganography methods mainly depend on human handcraft, while the proposed method could obtain from meachine learning. The embedding model consists of three neural networks: encoder which embeds the secret message in the carrier, decoder which extracts the message, and discriminator which determines the carriers containing secret messages. 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. Besides, the extensive experiments demonstrate the robustness and security of our algorithm.