The Rich Model of the Gabor filter (referred to as the GFR steganalytic feature) can detect JPEG-adaptive steganography objects. However, feature dimensionality that is too high will lead to too much computation and will correspondingly reduce the detection efficiency. To reduce the dimensionality and the operating time of GFR steganalytic features and to improve the stego image detection accuracy, this paper proposes a multi-scale feature selection method for steganalytic feature GFR. First, we use the SNR criterion to measure the uselessness of each feature component and to provide a basis for the removal of useless steganalytic feature components. Second, we improve the Relief algorithm to measure the importance of feature components in detecting stego images, which provides a basis for the selection of important feature components. Then, we set the threshold value for deleting the useless feature components, and we select the important feature components as the final feature. Finally, we conduct experiments on feature selection for GFR with high-dimensional steganalytic features, and we compared the proposed method with the Fisher-based method, the PCA-based method, the SSFC method, and the Steganalysis-α method. The results show that the method proposed in this paper is effective and fast.
The mutual confrontation between image steganography and steganalysis causes both to iterate continuously, and as a result, the dimensionality of the steganalytic features continues to increase, leading to an increasing spatio-temporal overhead. To this end, this paper proposes a fast steganalytic feature selection method based on a similar cross-entropy. Firstly, the properties of cross-entropy are investigated, through the discussion of different models, and the intra-class similarity criterion and inter-class similarity criterion based on cross-entropy are presented for the first time. Then, referring to the design principles of Fisher’s criterion, the criterion of feature contribution degree is further proposed. Secondly, the variation of the cross-entropy function of a univariate variable is analyzed in principle, thus determining the normalized range and simplifying the subsequent analysis. Then, within the normalized range, the variation of the cross-entropy function of a binary variable is investigated and the setting of important parameters is determined. Thirdly, the concept of similar cross-entropy is further presented by analyzing the changes in the value of the feature contribution measure under different circumstances, and based on this, the criterion for the feature contribution measure is updated to decrease the complexity of the calculation. Remarkably, the contribution measure criterion devised in this paper is a symmetrical structure, which equitably measures the contribution of features in different situations. Fourth, the feature component with the highest contribution is selected as the final selected feature based on the result of the feature metric. Finally, based on the Bossbase 1.01 image base that is a unique standard and recognized base in steganalysis, the feature selection on 8 kinds of low and high-dimensional steganalytic features is carried out. Through extensive experiments, comparison with several classic and state-of-the-art methods, the method designed in this paper attains competitive or even better performance in detection accuracy, calculation cost, storage cost and versatility.
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