Abstract Deep neural networks are used increasingly in daily life. Many models contain redundant parameters that render them susceptible to being exploited for transmitting secret data, thereby injecting malware or stealing sensitive data. Research on steganographic schemes for neural networks is therefore necessary. Existing steganalysis methods often require access to network parameters, while black-box approaches achieve limited accuracy. In this work, we propose a black-box steganalysis scheme that feeds a fixed sequence of images into the target network to extract implicit features, which are then used to train a steganalysis network. This is a black-box steganalysis framework without access to internal network parameter, leveraging output probabilities from fixed image sequences to capture model behavior and enabling steganography detection across diverse network architectures. Experiments show that our method improves accuracy by 6–39.65% compared to existing steganalysis schemes.

Recently, many compressed neural network models have been implemented on embedded platforms. However, there is still a lack of steganographic methods that utilizes these compressed models for covert communication. In this paper, we propose a steganographic method during the model compression process, embedding secret data into the model to create a steganographic network, which applies to most model compression methods, such as model quantization, model pruning, and model distillation. The secret data receiver can extract the secret data using a corresponding extraction network, while ordinary users remain unaware of the existence of the secret data, thereby preventing suspicion. The extraction network is utilized concurrently with model compression to embed secret data, minimizing the impact of data embedding. Experimental results validate the effectiveness of the steganographic network in terms of size reduction, inference time reduction, and high accuracy, and the effectiveness in terms of capacity, security, and robustness for steganography. To enhance understanding of our work, we have uploaded a set of application instances embedding abstract content tohttps://github.com/timedeadline/Model_Steganography_during_Model_Compression.

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 tohttps://github.com/timedeadline/ApplicationInstance.