Multi-source Data Hiding in Neural Networks

This paper proposes a multi-source data hiding scheme for neural networks, in which multiple senders can simultaneously transmit different secret data to a receiver using the same neural network. In our scheme, multiple senders execute data embedding in the overlapping position of a network, so that the existence of other senders can be concealed. Each sender uses a unique embedding key to scramble the parameters for embedding, preventing an attacker or other senders from pretending him. In addition, data embedding is achieved during the training process of the neural network instead of modifying the neural network after training. As a result, the operation of data embedding has a tiny impact on the original neural network. On the receiver side, the corresponding embedding key is used to extract the secret data, while additional decoding networks are unnecessary. Experiments verified the effectiveness and security of our scheme, including embedding capacity and undetectability.