Journal
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
Volume 19, Issue 5, Pages 2982-2997Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TDSC.2021.3079957
Keywords
Linguistics; Training; Security; Probability distribution; Statistical distributions; Generators; Generative adversarial networks; Linguistic steganography; natural language generation; adaptive probability distribution
Categories
Funding
- National Natural Science Foundation of China [61872368, 61802410]
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This article proposes a novel linguistic steganographic model based on adaptive probability distribution and generative adversarial network, which can hide secret messages in the generated text and improve the security performance of steganography.
Text has become one of the most extensively used digital media in Internet, which provides steganography an effective carrier to realize confidential message hiding. Nowadays, generation-based linguistic steganography has made a significant breakthrough due to the progress of deep learning. However, previous methods based on recurrent neural network have two deviations including exposure bias and embedding deviation, which seriously destroys the security of steganography. In this article, we propose a novel linguistic steganographic model based on adaptive probability distribution and generative adversarial network, which achieves the goal of hiding secret messages in the generated text while guaranteeing high security performance. First, the steganographic generator is trained by using generative adversarial network to effectively tackle the exposure bias, and then the candidate pool is obtained by a probability similarity function at each time step, which alleviates the embedding deviation through dynamically maintaining the diversity of probability distribution. Third, to further improve the security, a novel strategy that conducts information embedding during model training is put forward. We design various experiments from different aspects to verify the performance of the proposed model, including imperceptibility, statistical distribution, anti-steganalysis ability. demonstrate that our proposed model outperforms the current state-of-the-art steganographic schemes.
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