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Joint Image Data Hiding and Rate-Distortion Optimization in Neural Compressed Latent Representations

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MultiMedia Modeling (MMM 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14554))

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Abstract

We present an end-to-end learned image data hiding framework that embeds and extracts secrets in the latent representations of a neural compressor. The message encoder/decoder design is flexible to support multiples of 64-bit message lengths. We jointly optimize with the rate-distortion function of the neural codec, which reduces the embedded size overhead significantly. By leveraging a perceptual loss function, our approach simultaneously achieves high image quality and low bit-error rate. Our approach offers superior image secrecy in steganography and watermarking scenarios than existing techniques. Processing messages in the compressed domain has much lower complexity, and our method can achieve about 30 times acceleration. Furthermore, with the prevalence of IoT smart devices, machines can extract hidden data directly from the compressed domain without decoding. Our framework can benefit both secure communication and the coding-for-machines concept.

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Notes

  1. 1.

    https://github.com/ando-khachatryan/HiDDeN.

  2. 2.

    https://github.com/ChaoningZhang/Universal-Deep-Hiding.

  3. 3.

    https://github.com/tancik/StegaStamp.

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Acknowledgements

The authors would like to thank the NSTC of Taiwan and CITI SINICA for supporting this research under the grant numbers 111-2221-E-002-134-MY3 and Sinica 3012-C3447.

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Authors and Affiliations

  1. National Taiwan University, Taipei, 106319, Taiwan

    Chen-Hsiu Huang & Ja-Ling Wu

Authors
  1. Chen-Hsiu Huang
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  2. Ja-Ling Wu
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Correspondence to Chen-Hsiu Huang .

Editor information

Editors and Affiliations

  1. University of Amsterdam, Amsterdam, The Netherlands

    Stevan Rudinac

  2. Delft University of Technology, Delft, The Netherlands

    Alan Hanjalic

  3. Delft University of Technology, Delft, The Netherlands

    Cynthia Liem

  4. University of Amsterdam, Amsterdam, The Netherlands

    Marcel Worring

  5. Reykjavik University, Reykjavik, Iceland

    Björn Þór Jónsson

  6. Microsoft Research Lab – Asia, Beijing, China

    Bei Liu

  7. The University of Tokyo, Tokyo, Japan

    Yoko Yamakata

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Huang, CH., Wu, JL. (2024). Joint Image Data Hiding and Rate-Distortion Optimization in Neural Compressed Latent Representations. In: Rudinac, S., et al. MultiMedia Modeling. MMM 2024. Lecture Notes in Computer Science, vol 14554. Springer, Cham. https://doi.org/10.1007/978-3-031-53305-1_8

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  • DOI: https://doi.org/10.1007/978-3-031-53305-1_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-53304-4

  • Online ISBN: 978-3-031-53305-1

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