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Deep adaptive hiding network for image hiding using attentive frequency extraction and gradual depth extraction

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Abstract

Image hiding secures information security in multimedia communication. Existing deep image hiding methods usually process the secret and cover information at first, and then fuse such entire processed information. This complete and rough fusion pipeline, however, severely hinders the quality improvement of the stego and revealed secret images. This paper proposes a deep image hiding architecture, named Deep Adaptive Hiding Network (DAH-Net), to gradually extract and fuse the necessary secret and cover information at the frequency and the depth (layer) extents. Specifically, we propose the Attentive Frequency Extraction method for the DAH-Net to adaptively extract the necessary secret and cover information at the frequency level. The Gradual Depth Extraction method is further proposed for the DAH-Net to gradually extract and fuse the attentive frequency secret and cover information at the depth (layer) level of the deep image hiding network. Extensive experiment results demonstrate the proposed DAH-Net is more universal and achieves state-of-the-art performances in image hiding, watermarking, and photographic steganography.

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The data generated and analyzed during the current study are available from the corresponding author on reasonable request.

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Funding

This work was supported in part by Guangdong Shenzhen joint Youth Fund under Grant 2021A151511074, in part by Shenzhen Key Technical Project under Grant 2022N063, in part by National Key Research and Development Program of China under Project Number 2018AAA0100100, in part by the NSFC funds 62206073 and 62176077, in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2019Bl515120055, in part by the Shenzhen Key Technical Project under Grant 2022N001, 2020N046, in part by the Shenzhen Fundamental Research Fund under Grant JCYJ20210324132210025, and in part by the Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies (2022B1212010005).

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Authors and Affiliations

  1. Department of Computer Science, Harbin Institute of Technology Shenzhen, Xiaqing Street, Shenzhen, 518071, Guangdong, China

    Le Zhang, Yao Lu, Jinxing Li, Fanglin Chen & Guangming Lu

  2. School of Data Science, Chinese University of Hong Kong Shenzhen, Longxiang Street, Shenzhen, 518172, Guangdong, China

    David Zhang

  3. Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies, Shenzhen, China

    Guangming Lu

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  1. Le Zhang
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  2. Yao Lu
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  3. Jinxing Li
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  4. Fanglin Chen
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  5. Guangming Lu
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  6. David Zhang
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Correspondence to Guangming Lu.

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Zhang, L., Lu, Y., Li, J. et al. Deep adaptive hiding network for image hiding using attentive frequency extraction and gradual depth extraction. Neural Comput & Applic 35, 10909–10927 (2023). https://doi.org/10.1007/s00521-023-08274-w

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  • DOI: https://doi.org/10.1007/s00521-023-08274-w

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