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山东大学学报（理学版） ›› 2018, Vol. 53 ›› Issue (3): 63-70.doi: 10.6040/j.issn.1671-9352.2.2017.294

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一种基于浅层卷积神经网络的隐写分析方法

刘明明,张敏情,刘佳,高培贤   

  1. 武警工程大学网络与信息安全武警部队重点实验室, 陕西 西安 710086
  • 收稿日期:2017-08-28 出版日期:2018-03-20 发布日期:2018-03-13
  • 作者简介:刘明明(1992— ),男,硕士,研究方向为信息隐藏、深度学习. E-mail:1078491558@qq.com
  • 基金资助:
    国家自然科学基金资助项目(61379152;61403417)

Steganalysis method based on shallow convolution neural network

LIU Ming-ming, ZHANG Min-qing, LIU Jia, GAO Pei-xian   

  1. Key Laboratory of Network and Information Security Armed Police Force, Engineering University of the Armed Police Force, Xian 710086, Shaanxi, China
  • Received:2017-08-28 Online:2018-03-20 Published:2018-03-13

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摘要： 为提高隐写分析的检测准确率,提出了一种基于浅层卷积神经网络的图像隐写分析方法。与深度卷积神经网络相比,浅层卷积神经网络通过减少卷积层和禁用池化层,来加快神经网络收敛速度和减少隐写特征丢失,同时采用增加卷积核数、使用批正则化以及使用单层全连接层的方式,提高隐写分析网络的泛化性能。实验结果表明,针对S-UNIWARD隐写算法,在嵌入率为0.4 bpp和0.1 bpp时,检测准确率分别能达到96%和81.7%,同时在载体库源及嵌入率失配情况下,该方法仍能保持较好的检测性能。

关键词: 神经网络, 深度学习, 浅层卷积, 隐写分析

Abstract: In order to improve the detection rate of steganalysis, a method of image steganalysis based on shallow convolution neural network is proposed. Compared with the deep convolution neural network, the shallow convolution neural network can improve the convergence speed of the neural network and reduce the loss of the steganography feature by reducing the convolution layer and disabling the pool layer. At the same time, the generalization performance of the steganalysis network is improved by using batch normalization functions and using a single fully connected layer. The experimental results show that the detection accuracy can reach 96% and 81.7% respectively when the embedding rate is 0.4 bpp and 0.1 bpp. And the method is still maintain a better detection performance in the case of carrier source and embedding rate mismatch.

Key words: steganalysis, shallow convolution, neural network, deep learning

中图分类号: 

  • TP309
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