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 featu...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.展开更多
为筛选κ-酪蛋白优势基因型,本研究设计4个SNP位点,利用基因组检测已知CSN3基因型的13头牛,采集抗凝血,提取基因组DNA,采用竞争性等位基因特异性聚合酶链式反应(Kompetitive Allele Specific PCR,KASP)对样本进行分型,CSN3基因有AA、BB...为筛选κ-酪蛋白优势基因型,本研究设计4个SNP位点,利用基因组检测已知CSN3基因型的13头牛,采集抗凝血,提取基因组DNA,采用竞争性等位基因特异性聚合酶链式反应(Kompetitive Allele Specific PCR,KASP)对样本进行分型,CSN3基因有AA、BB、EE、AB、AE、BE六种基因型。本研究实现了对北京地区213头荷斯坦种公牛及1003头母牛中CSN3三种常见变异体的全面筛选,从育种角度出发,基于κ-酪蛋白不同基因型与奶酪生产的产量和质量相关性,利用分子检测方法筛选与优良性状的优势基因型,发掘荷斯坦牛特色优质种质资源,为牧场选择合适的荷斯坦牛,提高乳蛋白率,从种源解决相关领域的技术瓶颈。展开更多
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).
文摘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.
文摘为筛选κ-酪蛋白优势基因型,本研究设计4个SNP位点,利用基因组检测已知CSN3基因型的13头牛,采集抗凝血,提取基因组DNA,采用竞争性等位基因特异性聚合酶链式反应(Kompetitive Allele Specific PCR,KASP)对样本进行分型,CSN3基因有AA、BB、EE、AB、AE、BE六种基因型。本研究实现了对北京地区213头荷斯坦种公牛及1003头母牛中CSN3三种常见变异体的全面筛选,从育种角度出发,基于κ-酪蛋白不同基因型与奶酪生产的产量和质量相关性,利用分子检测方法筛选与优良性状的优势基因型,发掘荷斯坦牛特色优质种质资源,为牧场选择合适的荷斯坦牛,提高乳蛋白率,从种源解决相关领域的技术瓶颈。
