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。
In order to improve the detection rate of steganalysis,a method of image steganalysis based on shallowconvolution neural network is proposed. Compared with the deep convolution neural network,the shallowconvolution 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 showthat the detection accuracy can reach96% 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.
作者
刘明明
张敏情
刘佳
高培贤
LIU Ming-ming, ZHANG Min-qing, LIU Jia, GAO Pei-xian(Key Laboratory of Network and Information Security Armed Police Force, Engineering University of the Armed Police Force, Xi,an 710086, Shaanxi, Chin)
出处
《山东大学学报（理学版）》
CAS
CSCD
北大核心
2018年第3期63-70,共8页
Journal of Shandong University(Natural Science)
基金
国家自然科学基金资助项目(61379152
61403417)
关键词
浅层卷积
神经网络
隐写分析
深度学习
shallow convolution
neural network
steganalysis
deep learning
