IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i2p189-d482851.html
   My bibliography  Save this article

Image Steganalysis via Diverse Filters and Squeeze-and-Excitation Convolutional Neural Network

Author

Listed:
  • Feng Liu

    (College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China)

  • Xuan Zhou

    (College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China)

  • Xuehu Yan

    (College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China)

  • Yuliang Lu

    (College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China)

  • Shudong Wang

    (College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China)

Abstract

Steganalysis is a method to detect whether the objects contain secret messages. With the popularity of deep learning, using convolutional neural networks (CNNs), steganalytic schemes have become the chief method of combating steganography in recent years. However, the diversity of filters has not been fully utilized in the current research. This paper constructs a new effective network with diverse filter modules (DFMs) and squeeze-and-excitation modules (SEMs), which can better capture the embedding artifacts. As the essential parts, combining three different scale convolution filters, DFMs can process information diversely, and the SEMs can enhance the effective channels out from DFMs. The experiments presented that our CNN is effective against content-adaptive steganographic schemes with different payloads, such as S-UNIWARD and WOW algorithms. Moreover, some state-of-the-art methods are compared with our approach to demonstrate the outstanding performance.

Suggested Citation

  • Feng Liu & Xuan Zhou & Xuehu Yan & Yuliang Lu & Shudong Wang, 2021. "Image Steganalysis via Diverse Filters and Squeeze-and-Excitation Convolutional Neural Network," Mathematics, MDPI, vol. 9(2), pages 1-13, January.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:2:p:189-:d:482851
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/2/189/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/9/2/189/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:9:y:2021:i:2:p:189-:d:482851. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.