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Image Steganalysis Based on Deep Content Features Clustering

Computers, Materials & Continua 2023年第9期76卷 2921-2936页

作者： Chengyu Mo Fenlin Liu Ma Zhu gengcong yan Baojun Qi Chunfang yang Henan Provincial Key Laboratory of Cyberspace Situational Awareness Zhengzhou450001China Zhengzhou Science and Technology Institute Zhengzhou450001China School of Science Aalto UniversityEspoo02150Finland

The training images with obviously different contents to the detected images will make the steganalysis model perform poorly in deep *** existing methods try to reduce this effect by discarding some features related to image ***,this should lose much helpful information and cause low detection *** paper proposes an image steganalysis method based on deep content features clustering to solve this ***,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 ***,the extracted features are clustered to obtain the corresponding class labels to achieve sample ***,the steganalysis network is trained separately using samples in each subclass to achieve more reliable *** experimented on publicly available combined datasets of Bossbase1.01,Bows2,and ALASKA#2 with a quality factor of *** 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 *** selects the proper detector for the image to be *** results show that the pre-classification scheme can effectively obtain image subclasses with certain similarities and better ensure the consistency of training and testing *** 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

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