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Volume 37 Issue 5
September  2022
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Xiao-Qing Deng, Bo-Lin Chen, Wei-Qi Luo, Da Luo. Universal Image Steganalysis Based on Convolutional Neural Network with Global Covariance Pooling[J]. Journal of Computer Science and Technology, 2022, 37(5): 1134-1145. DOI: 10.1007/s11390-021-0572-0
Citation: Xiao-Qing Deng, Bo-Lin Chen, Wei-Qi Luo, Da Luo. Universal Image Steganalysis Based on Convolutional Neural Network with Global Covariance Pooling[J]. Journal of Computer Science and Technology, 2022, 37(5): 1134-1145. DOI: 10.1007/s11390-021-0572-0
shu

Universal Image Steganalysis Based on Convolutional Neural Network with Global Covariance Pooling

  • 1Guangdong Key Laboratory of Information Security Technology, Sun Yat-sen University, Guangzhou 510006, China
    2School of Cyberspace Security, Dongguan University of Technology, Dongguan 523808, China

Funds: The work was supported in part by the National Natural Science Foundation of China under Grant No. 61972430, the Natural Science Foundation of Guangdong Province of China under Grant No. 2019A1515011549, the Guangdong Natural Science Key Field Project under Grant No. 2019KZDZX1008.
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  • Author Bio:

    Wei-Qi Luo received his Ph.D. degree in computer science and technology from Sun Yat-sen University, Guangzhou, in 2008. He is currently a professor with the School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, where he is also a researcher with the Guangdong Key Laboratory of Information Security Technology, Guangzhou. His current research interests include digital multimedia forensics, steganography, and steganalysis.

  • Corresponding author:

    Wei-Qi Luo E-mail: luoweiqi@mail.sysu.edu.cn

  • Received Date: April 20, 2020
  • Revised Date: June 06, 2021
  • Accepted Date: June 28, 2021
  • Published Date: September 29, 2022
    Abstract
  • Recently, steganalytic methods based on deep learning have achieved much better performance than traditional methods based on handcrafted features. However, most existing methods based on deep learning are specially designed for one image domain (i.e., spatial or JPEG), and they often take long time to train. To make a balance between the detection performance and the training time, in this paper, we propose an effective and relatively fast steganalytic network called US-CovNet (Universal Steganalytic Covariance Network) for both {the} spatial and JPEG domains. To this end, we carefully design several important components of {US-CovNet} that will significantly affect the detection performance, including the high-pass filter set, the shortcut connection and the pooling {layer}. Extensive experimental results show that compared with the current best steganalytic networks (i.e., SRNet and J-YeNet), {US-CovNet} can achieve the state-of-the-art results for detecting spatial steganography and have competitive performance for detecting JPEG steganography. For example, the detection accuracy of US-CovNet is at least 0.56% higher than that of SRNet in the spatial domain. In the JPEG domain, US-CovNet performs slightly worse than J-YeNet in some cases with the degradation of less than 0.78%. However, the training time of US-CovNet is significantly reduced, which is less than 1/4 and 1/2 of SRNet and J-YeNet respectively.
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    • References (37)
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