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Comparison of DCT and Gabor Filters in Residual Extraction of CNN Based JPEG Steganalysis

  • Huilin Zheng
  • Xuan Li
  • Danyang Ruan
  • Xiangui KangEmail author
  • Yun-Qing Shi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11378)

Abstract

An effective feature selection method to capture the weak stego noise is essential to image steganalysis. In the conventional JPEG steganalysis, Gabor filter and DCT filter are both used for residual extraction. However, there are few comparisons in existing convolutional neural networks (CNNs) based JPEG steganalysis using Gabor filter or DCT filter in the pre-processing stage to extract residuals. In this paper, we compare the performance of DCT filter with Gabor filter in the pre-processing phase of the steganalysis CNN. Firstly, we choose the parameters empirically and theoretically for Gabor filters which are used in CNN. Secondly, we improve the performance by removing the ABS layer in the original XuNet. Finally, the experimental results show that using Gabor filters or DCT filter can achieve comparable performance whenever the parameters of pre-processing filters are fixed or learnable. It’s different from the conventional steganalysis method where Gabor filters have advantages over DCT filters. When the parameters of the pre-processing filters are learnable, both Gabor filter and DCT filter can achieve better performance compared with the condition where the parameters are fixed.

Keywords

JPEG steganalysis Gabor filter Convolutional neural networks (CNNs) 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Huilin Zheng
    • 1
  • Xuan Li
    • 1
  • Danyang Ruan
    • 1
  • Xiangui Kang
    • 1
    Email author
  • Yun-Qing Shi
    • 2
  1. 1.Guangdong Key Lab of Information Security Technology, School of Data and Computer ScienceSun Yat-sen UniversityGuangzhouChina
  2. 2.Department of ECENew Jersey Institute of TechnologyNewarkUSA

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