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Image Block Regression Based on Feature Fusion for CNN-Based Spatial Steganalysis

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Digital Forensics and Watermarking (IWDW 2021)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 13180))

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Abstract

The research on image steganalysis has taken a great step forward thanks to the development of convolutional neural network (CNN). However, most existing CNN steganalyzers require deep network architecture and a long training time to obtain good performance. Besides, most methods input a cover image and the corresponding stego image as a pair during the training stage. In this case, not only the label, but also the texture difference between the cover and the stego can be used. In this paper, we propose a novel method for spatial domain steganalysis to boost the detection performance through a secondary task. First, a concise and efficient CNN is used to distinguish stego images from cover images. Second, an up-sampling module via transposed convolution and feature fusion is used to predict the number of modified pixels in different blocks of the image, which is called block regression. The loss functions of these two parts above are optimized simultaneously. Experimental results show that the proposed method can obtain a better performance in detection error rate compared to previous works and a good trade-off between training time and detection accuracy.

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Authors and Affiliations

  1. School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China

    Ziqing Chen, Xiangyu Yu & Runze Chen

Authors
  1. Ziqing Chen
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  2. Xiangyu Yu
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  3. Runze Chen
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Corresponding author

Correspondence to Xiangyu Yu .

Editor information

Editors and Affiliations

  1. Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China

    Xianfeng Zhao

  2. University of Florence, Florence, Italy

    Alessandro Piva

  3. Universidade de Vigo, Vigo, Spain

    Pedro Comesaña-Alfaro

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Chen, Z., Yu, X., Chen, R. (2022). Image Block Regression Based on Feature Fusion for CNN-Based Spatial Steganalysis. In: Zhao, X., Piva, A., Comesaña-Alfaro, P. (eds) Digital Forensics and Watermarking. IWDW 2021. Lecture Notes in Computer Science(), vol 13180. Springer, Cham. https://doi.org/10.1007/978-3-030-95398-0_18

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  • DOI: https://doi.org/10.1007/978-3-030-95398-0_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-95397-3

  • Online ISBN: 978-3-030-95398-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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