General Steganography for Neural Network Models Based on Graph Convolutional Network

Abstract

In this article, our idea is to propose a general steganographic framework for neural network models, embedding secret data during the network training process to obtain a stego network for covert communication. A novelty of this method is that it can be applied to various types of neural networks, such as neural networks that perform image classification, image segmentation, image generation, and language generation tasks. Additionally, our method enables data embedding in different layers of neural networks, including linear layers, convolutional layers, and transpose convolutional layers. In cover networks, the hidden layer is transformed into a graph structure to facilitate data embedding using graph convolutional networks (GCNs). Another novelty is that the parameters of the GCN can be randomly initialized or directly specified. The connectivity of the graph structure is predetermined collaboratively by the sender and receiver, eliminating the need to transmit the GCN parameters and graph connectivity. Using our framework, embedding and extraction of secret data can be successfully applied to different layers of the stego network. Experimental results demonstrate that the proposed method offers higher security at the same capacity and exhibits sufficient robustness. To enhance understanding of our work, we have uploaded a set of application instances embedding abstract content to https://github.com/timedeadline/ApplicationInstance.