A general steganographic framework for neural network models

In this paper, we propose a general steganographic framework for neural networks to achieve covert communication. Firstly, we design a baseline steganographic method to embed secret data into convolutional layers of a given neural network (cover network) during the process of network training. With a network containing secret data (stego network), matrix multiplication is used to encode the parameters of convolutional layers for data extraction. Based on the baseline steganographic method, we respectively extended multi-source and multi-channel steganographic schemes for different scenarios, in which multiple agents are allowed to embed or extract secret data at the sender or receiver side. The multiple senders or receivers at the same side are ignorant of each other's existence, ensuring the independence and security of communication between each pair of sender and receiver. Experimental results verified the universality of the baseline method and the effectiveness of the extended schemes, including capacity, security, and robustness.