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Deep residual learning for image steganalysis

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Abstract

Image steganalysis is to discriminate innocent images and those suspected images with hidden messages. This task is very challenging for modern adaptive steganography, since modifications due to message hiding are extremely small. Recent studies show that Convolutional Neural Networks (CNN) have demonstrated superior performances than traditional steganalytic methods. Following this idea, we propose a novel CNN model for image steganalysis based on residual learning. The proposed Deep Residual learning based Network (DRN) shows two attractive properties than existing CNN based methods. First, the model usually contains a large number of network layers, which proves to be effective to capture the complex statistics of digital images. Second, the residual learning in DRN preserves the stego signal coming from secret messages, which is extremely beneficial for the discrimination of cover images and stego images. Comprehensive experiments on standard dataset show that the DRN model can detect the state of arts steganographic algorithms at a high accuracy. It also outperforms the classical rich model method and several recently proposed CNN based methods.

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Authors and Affiliations

  1. College of Computer Science and Software Engineering, Shenzhen University, Guangdong Sheng, China

    Songtao Wu & Shenghua Zhong

  2. Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong

    Yan Liu

Authors
  1. Songtao Wu
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  2. Shenghua Zhong
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  3. Yan Liu
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Corresponding author

Correspondence to Shenghua Zhong.

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Wu, S., Zhong, S. & Liu, Y. Deep residual learning for image steganalysis. Multimed Tools Appl 77, 10437–10453 (2018). https://doi.org/10.1007/s11042-017-4440-4

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  • DOI: https://doi.org/10.1007/s11042-017-4440-4

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