Imbalanced Steganalysis Method Based on Adaptive Cost-Sensitive Feature Learning

Abstract

The invention discloses an unbalanced steganalysis method based on adaptive cost-sensitive feature learning. The method aims at unbalanced JPEG image steganography detection environment, and strives to solve the problem of traditional steganalysis methods under the condition of unbalanced data distribution. The problem of greatly reduced effectiveness mainly implements the steganographic detection scheme for unbalanced data sets from the following three aspects. First, a preprocessing method for unbalanced samples is proposed, which determines the optimal k value of the dynamic k-nearest neighbor algorithm (DkNN) for each sample, and obtains the intra-class cost according to the proportion of the class; secondly, based on the intra-class cost of each training sample and the inter-class cost to generate an adaptive cost-sensitive classifier; finally, through the adaptive cost-sensitive classifier of F-measure maximization and regularized logistic regression and the acquisition of effective features, the features corresponding to the maximum F-measure can be obtained.

Description

technical field [0001] The invention relates to the technical field of multimedia security and digital media processing, in particular to the technical field of unbalanced steganographic detection for judging whether a JPEG image has been embedded with secret information when the number of samples of different categories in a training data set is greatly different. Background technique [0002] Steganography is a covert communication technique that embeds secret data into an image by modifying the image's pixels or frequency coefficients in an imperceptible manner [1] . Due to the wide application of the JPEG format, many steganographic algorithms have been designed for the JPEG domain. In contrast, image steganalysis focuses on the presence or absence of secret information in digital images [2] . Modern steganalysis algorithms are emerging, trying to identify some specific statistical features that can effectively distinguish cover and stego images. [0003] Although th...

AI Technical Summary

Problems solved by technology

[0013] The present invention aims at the problem that the reliability of the detection results of the existing steganalysis method decreases in an unbalanced environment, and realizes an unbalanced steganalysis scheme capable of learning effective features from an unbalanced data set

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