JZUS - Journal of Zhejiang University SCIENCE

Frontiers of Information Technology & Electronic Engineering

Accepted manuscript available online (unedited version)

SCFformer: a binary data hiding method against JPEG compression based on spatial channel fusion Transformer

Author(s): Xintao DUAN, Chun LI, Bingxin WEI, Guoming WU, Chuan QIN, Haewoon NAM
Affiliation(s): College of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China; more
Corresponding email(s): duanxintao@htu.edu.cn
Key Words: Binary data hiding; Against JPEG compression; DCT quantization; SCFformer

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Xintao DUAN, Chun LI, Bingxin WEI, Guoming WU, Chuan QIN, Haewoon NAM. SCFformer: a binary data hiding method against JPEG compression based on spatial channel fusion Transformer[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.2300762

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author="Xintao DUAN, Chun LI, Bingxin WEI, Guoming WU, Chuan QIN, Haewoon NAM",
journal="Frontiers of Information Technology & Electronic Engineering",
year="in press",
publisher="Zhejiang University Press & Springer",
doi="https://doi.org/10.1631/FITEE.2300762"
}

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%A Guoming WU
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doi="https://doi.org/10.1631/FITEE.2300762"

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A1 - Haewoon NAM
J0 - Frontiers of Information Technology & Electronic Engineering
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Abstract
Chinese Summary
Academic Network
Reviewer Comment

Abstract: To enhance information security during transmission over public channels, images are frequently employed for binary data hiding. Nonetheless, this data is vulnerable to distortion due to JPEG compression, leading to challenges in recovering the original binary data. Addressing this issue, this paper introduces a pioneering method for binary data hiding that leverages a combined spatial and channel attention Transformer, termed SCFformer, designed to withstand JPEG compression. This method employs a novel DCT quantization truncation mechanism during the hiding phase to bolster the stego image’s resistance to JPEG compression, utilizing spatial and channel attention to conceal information in less perceptible areas and thereby enhancing the model’s resistance to steganalysis. In the extraction phase, the DCT quantization stage mechanism minimizes secret image loss during compression, facilitating easier information retrieval. The incorporation of scalable modules adds flexibility, allowing for variable-capacity data hiding. Experimental findings validate the high security, large capacity, and flexibility of our scheme, alongside a marked improvement in binary data recovery post-JPEG compression, underscoring our method’s leading-edge performance.

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