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Audio steganography cover enhancement via reinforcement learning

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Abstract

Recent advancements in steganography analysis based on deep neural networks have led to the development of steganography schemes that incorporate deep network technology like adversarial example, GAN, and reinforce learning. However, most deep network-based steganography schemes are unable to ensure error-free extraction of secret information because of the design similar to information reconstruction. To address this issue, this work proposes a novel audio steganography cover enhancement framework that leverages two networks—a policy network and an environment network—to enhance the undetectability and imperceptibility of the steganographic audio. The proposed schemes utilize the reinforcement algorithm to optimize steganography cover modification for improving undetectability and imperceptibility. And our method guarantees 100% extracting accuracy by only enhancing on domains that do not affect information extraction. The experimental results demonstrate that our method significantly enhances the missing detection rate of the target audio steganography analysis network over 90% when resisting a steganalysis network, and meanwhile, our method has strong imperceptibility which can hardly be distinguished by human ears.

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Data availability

The dataset can be shared by the corresponding author upon request.

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Funding

This work was supported by the National Natural Science Foundation of China (Grant No. 61300055, 61901237), Zhejiang Natural Science Foundation (Grant No. LY20F020010), Ningbo Science and Technology Innovation Project (Grant No. 2022Z075), Ningbo Natural Science Foundation (Grant No. 202003N4089) and K.C. Wong Magna Fund in Ningbo University.

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Authors and Affiliations

  1. College of Information Science and Engineering, Ningbo University, No. 818 Fenghua Road, Jiangbei District, Ningbo, 315211, Zhejiang, China

    Peiwen Zhuo, Diqun Yan, Kaiyu Ying, Rangding Wang & Li Dong

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  1. Peiwen Zhuo
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  2. Diqun Yan
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  3. Kaiyu Ying
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  4. Rangding Wang
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  5. Li Dong
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Contributions

PZ, KY: Scheme and algorithm design, network architecture design, code implementation, paper writing DY, RW, LD: Reinforcement learning framework design, paper writing.

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Correspondence to Diqun Yan.

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Zhuo, P., Yan, D., Ying, K. et al. Audio steganography cover enhancement via reinforcement learning. SIViP 18, 1007–1013 (2024). https://doi.org/10.1007/s11760-023-02819-1

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