基于卷积神经网络的图像隐写分析方法

Image steganalysis based on convolution neural network

为了提高卷积神经网络(CNN)在图像隐写分析领域的分类效果,构建了一个新的卷积神经网络模型(steganalysis-convolutional neural networks,S-CNN)进行隐写分析。该模型采用两层卷积层和两层全连接层,减少了卷积层的层数;通过在激活函数前增加批量正规化层对模型进行优化,避免了模型在训练过程中陷入过拟合;取消池化层,减少嵌入信息的损失,从而提高模型的分类效果。实验结果表明,相比传统的图像隐写分析方法,该模型减少了隐写分析步骤,并且具有较高的隐写分析准确率。

In order to improve the recognition effect of convolutional neural networks( CNN) in image steganalysis,this paper constructed a new steganalysis-convolutional neural networks model( S-CNN) for steganalysis. The model reduced the number of layers of the convolution layer by using two layers of convolution layer and two layers of the whole connection layer. By adding the batch normalization layer to optimize the model before the activation function,to avoid the model in the training process into the over-fitting. The cancellation of the pool layer reduced the loss of embedded information,thereby improving the classification effect of the mode. The experimental results show that,compare with the traditional steganalysis methods,the proposed model reduces the steganalysis step and has higher steganalysis accuracy.