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DeepMIH: Deep Invertible Network for Multiple Image Hiding.

Zhenyu Guan, Junpeng Jing, Xin Deng

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |January 10, 2022
    PubMed
    Summary
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    This study introduces DeepMIH, a novel framework for multiple image hiding using invertible neural networks. DeepMIH achieves superior invisibility, security, and recovery accuracy for hidden images compared to existing methods.

    Area of Science:

    • Computer Vision
    • Digital Image Processing
    • Machine Learning

    Background:

    • Multiple image hiding is challenging due to potential contour shadows and color distortion.
    • Existing methods struggle with high-capacity hiding while maintaining image quality.

    Purpose of the Study:

    • To propose a novel framework, DeepMIH, for high-capacity and high-fidelity multiple image hiding.
    • To improve the invisibility and security of hidden images.
    • To achieve perfect recovery of all secret images.

    Main Methods:

    • Developed an invertible hiding neural network (IHNN) for reversible image concealment and revelation.
    • Cascaded IHNNs to accommodate multiple secret images.
    • Integrated an importance map (IM) module to guide hiding based on previous results.

    Related Experiment Videos

  • Proposed a low-frequency wavelet loss to optimize hiding in high-frequency sub-bands.

    • Main Results:

    • DeepMIH significantly outperforms state-of-the-art methods in invisibility, security, and recovery accuracy.
    • The proposed IHNN framework effectively models reversible image hiding processes.
    • The IM module and wavelet loss enhance hiding performance and invisibility.

    Conclusions:

    • DeepMIH provides a flexible and effective solution for multiple image hiding.
    • The framework demonstrates superior performance across various datasets.
    • Invertible neural networks offer a promising direction for advanced steganography techniques.