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Preprocessing Enhancement Method for Spatial Domain Steganalysis

Author

Listed:
  • Xueming Duan

    (College of Science, North China University of Science and Technology, Tangshan 063210, China)

  • Chunying Zhang

    (College of Science, North China University of Science and Technology, Tangshan 063210, China
    Key Laboratory of Data Science and Application of Hebei Province, Tangshan 063210, China)

  • Yingshuo Ma

    (College of Science, North China University of Science and Technology, Tangshan 063210, China)

  • Shouyue Liu

    (College of Science, North China University of Science and Technology, Tangshan 063210, China)

Abstract

In the field of steganalysis, in recent years, the research focus has mostly been on optimizing the structures of neural networks, while the application of high-pass filters is still limited to the simple selection of filters and simple adjustment of the number of filters. In this paper, we propose a method to enhance the assistance and contribution of high-pass filters to the detection capability of a spatial domain steganalysis model, which mainly contains the preprocessing enhancement of high-pass filters and cross-layer enhancement of high-pass filters, and we construct a preprocessing enhancement model, the HPF-Enhanced Model, for spatial domain steganalysis, based on Yedroudj-Net. In the experimental part, we find the best preprocessing enhancement method through various validations, and we compare the HPF-Enhanced Model with the classical models. The results show that the proposed enhancement method can bring a significant improvement, and they also show that the preprocessing enhancement method can help to reduce the model size, and it thus can be used to construct a lightweight spatial domain steganalysis model with strong performance.

Suggested Citation

  • Xueming Duan & Chunying Zhang & Yingshuo Ma & Shouyue Liu, 2022. "Preprocessing Enhancement Method for Spatial Domain Steganalysis," Mathematics, MDPI, vol. 10(21), pages 1-12, October.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:21:p:3936-:d:951135
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