基于W2ID准则的Rich Model隐写检测特征选取方法

数字隐写是信息安全领域一个重要分支,其通过将秘密信息嵌入到数字图像、声音、视频等文件中并通过公开信道(如:Email邮箱、微博推文和即时通信等)进行传递,从而实现信息的隐蔽通信.图像自适应隐写是近年来数字隐写技术的研究热点,而Rich Model特征是检测图像自适应隐写的一大类主流高维特征,这类高维特征在实现对图像自适...

W2ID Criterion-Based Rich Model Steganalysis Features Selection

Digital steganography is an important branch of the information security technology.Digital steganography embeds the secret information into the digital files,which includes image,voice,video and others,and transmits them through public channels(e.g.E-mail,Twitter,Instant messaging,etc.),so as to realize transmitting the information in secret.The image adaptive steganography is a research hotspot in the area of steganography in recent years,while the Rich Model steganalysis feature is the mainstream high-dimensional feature for detecting image adaptive steganography.This kind of high dimension steganalysis feature not only achieves high detection accuracy of image adaptive steganography,but also brings the high computational overhead and storage space.Thus,the classifier in steganalysis is very difficult to train.For this reason,this paper proposes a steganalysis feature selection method(W2ID-a) based on the Weight Inter-class Distance and Intra-class distance Difference criterion(W2ID criterion).First,this paper analyzes the principle of the Fisher-based method,which is a classical steganalysis feature selection method.Then,it points out that this method may have the shortcomings of deleting useful feature components,retaining redundant and conflicting feature components by mistake.Second,by introducing the principle of "Intra-class distance Difference" to separability measurement of the steganalysis feature component,this paper proposes a separability measurement criterion based on both Interclasses Distance and Intra-class distance Difference(2ID criterion).The properties of Intra-class aggregation Difference are also given.Meanwhile,in order to highlighting the importance of the "Inter-class Distance",a weight assignment algorithm based on the frequency statistical weighting method is proposed to assign weights to the separability criterion reasonably.This criterion is called W2ID criterion.Thus,the separability result of the steganalysis feature component can be measured by the W2ID criterion more accurate than that of the traditional Fisher criterion.Finally,According to the measurement results based on the W2ID criterion,this method selects the steganalysis feature components based on the decision rough set a-positive region reduction method.In the process of feature component reduction,in order to improve the reduction efficiency,an improved decision rough set α-positive region reduction is proposed.The improved decision rough set a-positive region reduction method,which is deal with one feature component at a time,is changed to deal with one group of feature components at a time.Thus,useful feature components will be remained and the lower limit will not be set,by which the dependency of empirical parameters of the existing Steganalysis-α method can be eliminated.Two kinds of typical Rich Model steaganalysis features(35 263-D J+SRM feature and 17 000-D GFR feature) are extract from both 10000 original images in the BOSSbase-1.01 image database and multiple groups of stego images generated by classical SI-UNIWARD steganography algorithm.Then,a series of feature selection experiments are carried on the J+SRM feature and the GFR feature.The experimental results show that the proposed W2ID-α method can significantly reduce dimensions of Rich Model steaganalysis feature,meanwhile increase the accuracy to detect stego images.Compared with the results of existing typical feature selection methods,i.e.Fisherbased,Steganalysis-a method and PCA-based method,the proposed W2ID-α method has significant advantages.For example,for the stego image with payload 10% generated by SI-UNIWARD steganography,the dimension of J+SRM feature is reduced based on the proposed W2ID-α method from 35263 to 2723,and the detection accuracy is improved by 3.63%.