Abstract
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.
Highlights
Image steganography [1,2,3,4] refers to the use of some algorithm to embed secret information in an image for covert communication
Ma et al [24] proposed a feature selection method based on decision rough set α-positive domain approximation, which first applied rough set theory to the steganalytic feature selection, using the attribute separability measure (ASM) criterion to measure the divisibility of feature components, which was extended to measure the divisibility of feature vectors, and the final dominant features were selected based on the classifier, and the experiments demonstrated that the method significantly reduced the dimensionality of some features
In order to measure the contribution of a single feature component, this paper considers the cross-entropy principle as a guide to constructing a similar cross-entropy-based feature contribution criterion
Summary
Image steganography [1,2,3,4] refers to the use of some algorithm to embed secret information in an image for covert communication. In order to solve the above problems, this paper attempts to devise a fast selection method for steganalytic features based on similarity cross-entropy (FSCE). In order to verify the effectiveness and efficiency of FSCE, a large number of experiments are carried out in Bossbase 1.01 image database [36] (That contains 10,000 gray image pictures whose size is 512 × 512.), which is the only standard and recognized image base in the steganalysis field. This includes: Firstly, a comparison was made with the features selected under different thresholds to determine the correctness of the final selection threshold in this paper.
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