Steganography With Constructing Neural Networks

In recent years, due to the rich parameters contained in deep neural network (DNN) models, researchers have proposed DNN model steganography, using DNN models as carriers. In this paper, we propose a constructive DNN model steganography method based on parameter initialization. Unlike existing DNN model steganography methods that embed secret information into a pre-trained DNN model, we generate parameters containing secret information through parameter initialization for a DNN model structure, and the embedded secret information can still be extracted after model training. Specifically, we first generate the model parameters needed for the DNN model structure through a secret information-driven encoder, and then we jointly train the encoder and decoder to ensure the correct extraction of secret information. Additionally, we introduce a noise layer to simulate the model training process to guarantee the robustness of our method. Experimental results demonstrate that our method not only achieves high hiding capacity but also exhibits satisfactory stealthiness and robustness. Furthermore, our method is generalizable, which can be applied to various network structures, such as multilayer perceptron (MLP), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and Transformers.