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Steganalysis of Adaptive Multi-Rate Speech Based on Extreme Gradient Boosting

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College of Computer Science and Technology, National Huaqiao University, Xiamen 361021, China
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State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China
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Fujian Key Laboratory of Big Data Intelligence and Security, National Huaqiao University, Xiamen 361021, China
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Department of Information and Computer Science, Feng Chia University, Taichung 40724, Taiwan
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School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China
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Information and Engineering College, Jimei University, Fujian 361021, China
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Authors to whom correspondence should be addressed.
Electronics 2020, 9(3), 522; https://doi.org/10.3390/electronics9030522
Received: 9 December 2019 / Revised: 6 March 2020 / Accepted: 16 March 2020 / Published: 21 March 2020
(This article belongs to the Special Issue Deep Learning for the Internet of Things)
Steganalysis of adaptive multi-rate (AMR) speech is a hot topic for controlling cybercrimes grounded in steganography in related speech streams. In this paper, we first present a novel AMR steganalysis model, which utilizes extreme gradient boosting (XGBoost) as the classifier, instead of support vector machines (SVM) adopted in the previous schemes. Compared with the SVM-based model, this new model can facilitate the excavation of potential information from the high-dimensional features and can avoid overfitting. Moreover, to further strengthen the preceding features based on the statistical characteristics of pulse pairs, we present the convergence feature based on the Markov chain to reflect the global characterization of pulse pairs, which is essentially the final state of the Markov transition matrix. Combining the convergence feature with the preceding features, we propose an XGBoost-based steganalysis scheme for AMR speech streams. Finally, we conducted a series of experiments to assess our presented scheme and compared it with previous schemes. The experimental results demonstrate that the proposed scheme is feasible, and can provide better performance in terms of detecting the existing steganography methods based on AMR speech streams. View Full-Text
Keywords: steganalysis; steganography; convergence feature; extreme gradient trees; adaptive multi-rate speech steganalysis; steganography; convergence feature; extreme gradient trees; adaptive multi-rate speech
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Sun, C.; Tian, H.; Chang, C.-C.; Chen, Y.; Cai, Y.; Du, Y.; Chen, Y.-H.; Chen, C.C. Steganalysis of Adaptive Multi-Rate Speech Based on Extreme Gradient Boosting. Electronics 2020, 9, 522.

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