Mr. Likai Zeng Profile

Likai Zeng

at Dalian Univ of Technology

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Proceedings Article | 4 March 2015 Paper

JPEG quantization table mismatched steganalysis via robust discriminative feature transformation

Likai Zeng, Xiangwei Kong, Ming Li, Yanqing Guo

Proceedings Volume 9409, 94090U (2015) https://doi.org/10.1117/12.2078188

KEYWORDS: Quantization, Steganalysis, Transform theory, Image quality, Detection and tracking algorithms, Steganography, Databases, Image compression, Cameras, Evolutionary algorithms

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The cover source mismatch is a common problem in steganalysis, which may result in the degradation of detection accuracy. In this paper, we present a novel method to mitigate the problem of JPEG quantization table mismatch, named as Robust Discriminative Feature Transformation (RDFT). RDFT transforms original features to new feature representations based on a non-linear transformation matrix. It can improve the statistical consistency of the training samples and testing samples and learn new matched feature representations from original features by minimizing feature distribution difference while preserving the classification ability of training data. The comparison to prior arts reveals that the detection accuracy of the proposed RDFT algorithm can significantly outperform traditional steganalyzers under mismatched conditions and it is close to that of matched scenario. RDFT has several appealing advantages: 1) it can improve the statistical consistency of the training and testing data; 2) it can reduce the distribution difference between the training features and testing features; 3) it can preserve the classification ability of the training data; 4) it is robust to parameters and can achieve a good performance under a wide range of parameter values.

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