Data Hiding in Neural Networks for Multiple Receivers [Research Frontier]

Recently, neural networks have become a promising architecture for some intelligent tasks. In addition to conventional tasks such as classification, neural networks can be used for data hiding. This paper proposes a data hiding scheme to transmit different data to multiple receivers via the same neural network simultaneously. Additional data are embedded into the neural network during the process of training instead of modifying the network parameters after training. As a result, the reduction in detection accuracy of the neural network is trivial with additional data embedded. On the receiver side, additional data can be extracted using specific data decoding networks. Instead of training, the parameters of data decoding networks are generated using the embedding keys possessed by the receivers. Therefore, it is unnecessary to store and transmit the decoding networks secretly, and the practicability is satisfactory. A receiver can extract corresponding additional data with the correct embedding key. For other parts of additional data, the receiver cannot affirm their existence and therefore cannot extract them. Experimental results verify the effectiveness of the proposed data hiding scheme, including embedding capacity and robustness.