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High-Capacity Image Hiding via Compressible Invertible Neural Network

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Algorithms and Architectures for Parallel Processing (ICA3PP 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15252))

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Abstract

Image steganography involves the concealment of confidential information within images, rendering it undetectable to unauthorized observers, and subsequently retrieving the hidden information following secure transmission. Numerous investigations into image steganography employ invertible neural networks. Nevertheless, the intricate architecture of these models poses significant challenges to both their training and inference processes. Consequently, this paper proposes a high-capacity image steganography scheme utilizing compressible modules. We propose a multi-scale attention module that compresses the model structure through structural reparameterization post-training, thereby enhancing inference speed. Additionally, we employ a pre-trained image autoencoder to extract deep features from large-scale images, facilitating high-capacity steganography within invertible neural networks by concealing key features. Experimental results indicate that our approach offers superior image quality and model inference speed, while significantly enhancing security relative to existing methodologies.

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Acknowledgements

This research was funded by the National Natural Science Foundation of China under Grant 62462012, and Science and Technology Program of Hebei under Grant 22567606H.

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Authors and Affiliations

  1. College of Computer and Cyber Security, Hebei Normal University, Shijiazhuang, 050024, China

    Changguang Wang, Haoyi Shi, Qingru Li, Dongmei Zhao & Fangwei Wang

  2. Hebei Key Laboratory of Network and Information Security, Shijiazhuang, 050024, China

    Changguang Wang, Qingru Li, Dongmei Zhao & Fangwei Wang

Authors
  1. Changguang Wang
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  2. Haoyi Shi
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  3. Qingru Li
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  4. Dongmei Zhao
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  5. Fangwei Wang
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Corresponding author

Correspondence to Fangwei Wang .

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Editors and Affiliations

  1. City University of Macau, Macau, China

    Tianqing Zhu

  2. Guangzhou University, Guangzhou, China

    Jin Li

  3. University of Salerno, Fisciano, Italy

    Aniello Castiglione

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Wang, C., Shi, H., Li, Q., Zhao, D., Wang, F. (2025). High-Capacity Image Hiding via Compressible Invertible Neural Network. In: Zhu, T., Li, J., Castiglione, A. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2024. Lecture Notes in Computer Science, vol 15252. Springer, Singapore. https://doi.org/10.1007/978-981-96-1528-5_3

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  • DOI: https://doi.org/10.1007/978-981-96-1528-5_3

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  • Print ISBN: 978-981-96-1527-8

  • Online ISBN: 978-981-96-1528-5

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