Feature Bagging for Steganographer Identification
Hanzhou Wu

TL;DR
This paper introduces a feature bagging approach for steganographer identification that improves detection accuracy by combining multiple models trained on randomly sampled feature subsets, addressing high-dimensional feature space challenges.
Contribution
The paper proposes a novel feature bagging method for steganographer identification, enhancing detection robustness and accuracy in high-dimensional feature spaces.
Findings
Significant improvement in detection accuracy on the ImgNetEase dataset.
Effective handling of high-dimensional feature space issues.
Demonstrated superiority over existing methods in experiments.
Abstract
Traditional steganalysis algorithms focus on detecting the existence of steganography in a single object. In practice, one may face a complex scenario where one or some of multiple users also called actors are guilty of using steganography, which is defined as the steganographer identification problem (SIP). This requires steganalysis experts to design effective and robust detection algorithms to identify the guilty actor(s). The mainstream works use clustering, ensemble and anomaly detection, where distances in high dimensional space between features of actors are determined to find out the outlier(s) corresponding to steganographer(s). However, in high dimensional space, feature points could be sparse such that distances between feature points may become relatively similar to each other, which cannot benefit the detection. Moreover, it is well-known in machine learning that combining…
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Taxonomy
TopicsAdvanced Steganography and Watermarking Techniques · Digital Media Forensic Detection · Internet Traffic Analysis and Secure E-voting
