Image Steganalysis Based on Deep Content Features Clustering  

作　　者：Chengyu Mo Fenlin Liu Ma Zhu Gengcong Yan Baojun Qi Chunfang Yang 

机构地区：[1]Henan Provincial Key Laboratory of Cyberspace Situational Awareness,Zhengzhou,450001,China [2]Zhengzhou Science and Technology Institute,Zhengzhou,450001,China [3]School of Science,Aalto University,Espoo,02150,Finland

出　　处：《Computers, Materials & Continua》2023年第9期2921-2936,共16页计算机、材料和连续体（英文）

基　　金：supported by the National Natural Science Foundation of China(Nos.61872448,62172435,62072057);the Science and Technology Research Project of Henan Province in China(No.222102210075).

摘　　要：The training images with obviously different contents to the detected images will make the steganalysis model perform poorly in deep steganalysis.The existing methods try to reduce this effect by discarding some features related to image contents.Inevitably,this should lose much helpful information and cause low detection accuracy.This paper proposes an image steganalysis method based on deep content features clustering to solve this problem.Firstly,the wavelet transform is used to remove the high-frequency noise of the image,and the deep convolutional neural network is used to extract the content features of the low-frequency information of the image.Then,the extracted features are clustered to obtain the corresponding class labels to achieve sample pre-classification.Finally,the steganalysis network is trained separately using samples in each subclass to achieve more reliable steganalysis.We experimented on publicly available combined datasets of Bossbase1.01,Bows2,and ALASKA#2 with a quality factor of 75.The accuracy of our proposed pre-classification scheme can improve the detection accuracy by 4.84%for Joint Photographic Experts Group UNIversal WAvelet Relative Distortion(J-UNIWARD)at the payload of 0.4 bits per non-zero alternating current discrete cosine transform coefficient(bpnzAC).Furthermore,at the payload of 0.2 bpnzAC,the improvement effect is minimal but also reaches 1.39%.Compared with the previous steganalysis based on deep learning,this method considers the differences between the training contents.It selects the proper detector for the image to be detected.Experimental results show that the pre-classification scheme can effectively obtain image subclasses with certain similarities and better ensure the consistency of training and testing images.The above measures reduce the impact of sample content inconsistency on the steganalysis network and improve the accuracy of steganalysis.

关 键 词：STEGANALYSIS deep learning pre-classification 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]