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To address the problem of low detection accuracy of traditional Bayesian network methods in compressed speech quantization index modulation steganalysis with low embedding rates, a steganalysis method based on Bayesian network ensembles was proposed. Firstly, Bayesian network ensembles were constructed to describe the correlations among speech codewords themselves, within frames, and between frames, and a conditional probability table was built through overall sample learning. Then, the feature vector of individual samples was constructed using the inference results of each sub-network, and these features were used to train a support vector machine(SVM) model. Finally, the steganalysis classification of unknown samples was achieved. Experimental results showed that on a 10 s Chinese and English speech dataset, with an embedding rate of 20%, this method improved the detection accuracy by at least 18.01 percentage points and 2.32 percentage points compared with traditional Bayesian network methods and deep learning methods, respectively. Moreover, the average duration for detecting 1 s of speech using this method was 2.72 ms, meeting the requirements for real-time detection.
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Basic Information:
DOI：10.13705/j.issn.1671-6841.2024113
China Classification Code:TP391.41
Citation Information:
[1]GAO Feipeng,YANG Jie.Steganalysis Based on Bayesian Network Ensembles for Compressed Speech Quantization Index Modulation[J].Journal of Zhengzhou University(Natural Science Edition),2025,57(06):34-41.DOI:10.13705/j.issn.1671-6841.2024113.
Fund Information:
浙江省自然科学基金项目(LQ20F020004)
2024-06-20
2024
2025-08-28
2025
2024-12-02
2
2024-11-13
2024-11-13
2024-11-13