小波变换和神经网络相融合的图像隐写分析

Image Steganalysis Based on Wavelet Transform and Neural Network

如何设计高性能的图像隐写分析算法仍是一个难题,为了获得更加理想的图像隐写检测结果,提出一种小波变换和神经网络相融合的图像隐写分析算法。首先采用小波变换对图像进行去噪处理,然后提取图像的共生矩阵特征,然后采用卷积神经网络对特征向量进行训练,建立图像隐写分析的分类器,最后采用仿真实验测试算法的性能。实验结果表明,本文算法提高了图像隐写分析的正确率,而且检测速度要优于当前常用图像隐写分析算法。

How to design a high performance image steganalysis algorithm is still a problem,in order to obtain the more ideal image steganalysis detection results,this paper puts forward an image steganalysis algorithm based on neural network and wavelet transform. Firstly,wavelet transform is used to denoise image,and the co-occurrence matrix features of images are extracted. Then convolutional neural networks is used for training the feature and establishing image steganalysis classifier. Finally,the performance is tested by simulation experiments,The experimental results show that the proposed algorithm can improve the accuracy rate of image steganalysis and the detection speed is superior to the current image steganalysis algorithms.