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Model Steganography During Model Compression
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AI
DOI：10.1109/TDSC.2025.3645268.png)
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Abstract
En 中文
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.
Keywords:
Steganography
covert communication
neural networks
security
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7.5
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2.6K
Citations:
9.6K
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