Detecting Steganography of Adaptive Multirate Speech with Unknown Embedding Rate

Hui Tian,Yonghong Chen,Jun Sun,Yongfeng Huang,Tian Wang,Yiqiao Cai

doi:10.1155/2017/5418978

Hui Tian, Yonghong Chen + Show 4 more

Open Access

https://doi.org/10.1155/2017/5418978

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Abstract

Steganalysis of adaptive multirate (AMR) speech is a significant research topic for preventing cybercrimes based on steganography in mobile speech services. Differing from the state-of-the-art works, this paper focuses on steganalysis of AMR speech with unknown embedding rate, where we present three schemes based on support-vector-machine to address the concern. The first two schemes evolve from the existing image steganalysis schemes, which adopt different global classifiers. One is trained on a comprehensive speech sample set including original samples and steganographic samples with various embedding rates, while the other is trained on a particular speech sample set containing original samples and steganographic samples with uniform distributions of embedded information. Further, we present a hybrid steganalysis scheme, which employs Dempster–Shafer theory (DST) to fuse all the evidence from multiple specific classifiers and provide a synthesized detection result. All the steganalysis schemes are evaluated using the well-selected feature set based on statistical characteristics of pulse pairs and compared with the optimal steganalysis that adopts specialized classifiers for corresponding embedding rates. The experimental results demonstrate that all the three steganalysis schemes are feasible and effective for detecting the existing steganographic methods with unknown embedding rates in AMR speech streams, while the DST-based scheme outperforms the others overall.

Highlights

Steganography is an ancient but effective technique for covert communications through hiding confidential messages into seemingly innocent carriers with imperceptible distortion
Due to its increasing popularity and broad influence in mobile communications, adaptive multirate (AMR) speech is spontaneously considered as an ideal carrier by the steganographic research community, and some relevant studies have been successfully performed [29,30,31,32,33]
The experimental results show that all these steganalysis schemes are feasible and efficient for detecting the state-of-the-art steganographic methods with unknown embedding rates in AMR speech streams, while the Dempster–Shafer theory (DST)-based scheme can achieve better detection performance than the other ones

Summary

Introduction

Steganography is an ancient but effective technique for covert communications through hiding confidential messages into seemingly innocent carriers with imperceptible distortion. In the image steganalysis field, some pioneer researchers have presented two useful schemes for detecting image steganography with unknown embedding rate Both the two schemes adopt global classifiers based on a machine-learning algorithm (e.g., SVM) as the detectors, but the components of their training set are different. The main idea behind the presented steganalysis scheme is employing an algorithm based on DST to combine all the evidence from a set of classifiers intended for detecting steganographic approaches with specific embedding rates and providing a synthesized judgement for having or not having hidden information. The experimental results show that all these steganalysis schemes are feasible and efficient for detecting the state-of-the-art steganographic methods with unknown embedding rates in AMR speech streams, while the DST-based scheme can achieve better detection performance than the other ones.

Steganalysis Features Based on Statistical Characteristics of Pulse Pairs

Performance Evaluation and Analysis

Steganographic methods

Findings

Conclusions