Steganalysis Feature Selection With Multidimensional Evaluation and Dynamic Threshold Allocation

Abstract

Steganalysis feature selection shows excellent effectiveness on elevating the detection efficiency and decreasing time-space cost. However, the single evaluation criterion for features and the subjective selection basis always lead to valuable features neglect, which restricts the improvement of detection accuracy. To alleviate this predicament, this paper proposes a steganalysis feature selection method based on multidimensional evaluation and dynamic threshold allocation (MEDTA method). Firstly, to measure the feature components’ contribution degree to detection, the concept of partial entropy for steganalysis (ste- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$pe$ </tex-math></inline-formula> ) is defined and utilized to measure the mutual information between feature components. On this basis, the evaluation criterion for steganalysis feature components’ contribution degree is proposed, and the theoretical basis is given. Secondly, to measure the functional similarity of the feature components in distinguishing between cover images and stego images, by applying the property of cosine similarity between vectors, the evaluation criterion for steganalysis feature components’ contribution angle is proposed. Then, according to the Occam’s Razor, a multidimensional evaluation criterion based on contribution degree and contribution angle is proposed, which provides a basis for feature selection. In addition, to allocate the threshold for feature selection, this paper proposed a dynamic threshold allocation model, which combines the merits of several function models. Finally, feature selection with multidimensional evaluation and dynamic threshold allocation is proposed, which can achieve a comprehensive evaluation and objective selection for steganalysis features. Extensive experiments conducted on the BOSSbase1.01 image database demonstrate that the proposed MEDTA method could not only achieve highly competitive or even better performance in detection accuracy and feature dimension reduction, as compared with the state-of-the-art methods, but also get rid of depending on classifiers, so that the efficiency of feature extraction and steganalysis gets promoted.

Topics & Concepts