Ensemble Steganalysis Based on Deep Residual Network

  • Qiangjie Li
  • Guorui FengEmail author
  • Hanzhou Wu
  • Xinpeng Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12022)


The performance of the steganography detector built on deep learning has been superior to the traditional feature-based methods, and more adaptive methods for steganalysis are beginning to emerge. However a single model may encounter a bottleneck in classification accuracy due to the absent diversity of training data and parameter configuration, it maybe fails to exert a strong fitting performance of the deep learning network. To make full use of the classification performance of the combination of multiple models, we first obtained multiple base learners from different snapshot and different training sets. Then two strategies to combine multiple base learners: one achieves the optimal ensemble effect by majority voting and product combination, another, in view of the insufficient performance of Softmax classifier, propose a scheme of feature extraction based on convolutional neural network. Experiments show that the ensemble scheme proposed can well fuse the output of multiple convolutional neural networks, thus effectively reduce the detection error rate of a single model.


Steganalysis Ensemble Feature fusion Convolutional neural networks 



This work was partly supported by the Natural Science Foundation of Shanghai under Grant 19ZR1419000 and National Natural Science Foundation of China under Grants 61902235, U1936214, U1636206, 61525203.


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Qiangjie Li
    • 1
  • Guorui Feng
    • 1
    Email author
  • Hanzhou Wu
    • 1
  • Xinpeng Zhang
    • 1
  1. 1.School of Communication and Information EngineeringShanghai UniversityShanghaiChina

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