A posterior evaluation algorithm of steganalysis accuracy inspired by residual co-occurrence probability. (March 2019)

• Record Type: Journal Article
• Authors: Wang, Lina; Xu, Yibo; Zhai, Liming; Ren, Yanzhen; Du, Bo
• Is Part Of: Pattern recognition. Volume 87(2019:Mar.)
• Page Start: 106
• Page End: 117
• Publication Date: 2019-03
• Subjects: Steganography -- Steganalysis -- Carrier security -- Adaptive convolution -- Posterior accuracy; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2018.10.003
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: Found and molded the relationship between the high-frequency signal of image carrier and steganalysis accuracy. Posterior accuracy is proposed to evaluate the real accuracy of steganalysis and it can be used to improve steganalysis performance in the actual environment, because we can find a subset with a high accuracy(such as 100%) from the prediction images which have a relatively low accuracy (such as 80%). Actually, the relationship between image carrier and steganalysis accuracy can even be used to improve steganography performance significantly. Abstract: Steganalysis research is committed to distinguishing the steganography media from the normal one correctly. However, individual differences of carriers disturb the detection inevitably and greatly. Existing methods treat all detection results with the same confidence level, or prior accuracy, which may make the prior accuracy overestimate or underestimate the real result. This paper presents a novel performance evaluation method of steganalysis based on posterior accuracy. Adaptive Convolution Feature ( ACF ) is calculated by the adaptive convolution, then a quantitative value S based on the ACF is modeled to posterior testify and estimate the detection confidence of the image under test. By clustering of carrier noise, we classify the images which we believe they have a similar confidence of the same cluster. The distribution of ACF from each cluster indicates that the S value works well for confidence evaluation. The experimental results show that the S value can distinguish and identify high-confidence samples from the low-confidence, which will greatly improve the real performance of steganalysis. Besides, it improves the steganalysis accuracy of the whole image set by matching S values between training and prediction samples.