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Existence detection and embedding rate estimation of blended speech in covert speech communications.

Li L, Gao Y - Springerplus (2016)

Bottom Line: The average zero crossing rate (ZCR) is calculated for each OED frame, and the minimum average ZCR and AZCR-OED of the entire speech signal are extracted as features.The results demonstrate that without attack, the detection accuracy can reach 80 % or more when the embedding rate is greater than 10 %, and the estimated embedding rate is similar to the real value.And when some attacks occur, it can also reach relatively high detection accuracy.

View Article: PubMed Central - PubMed

Affiliation: College of Electronics and Information Engineering, Sichuan University, Chengdu, 610064 Sichuan China.

ABSTRACT
Covert speech communications may be used by terrorists to commit crimes through Internet. Steganalysis aims to detect secret information in covert communications to prevent crimes. Herein, based on the average zero crossing rate of the odd-even difference (AZCR-OED), a steganalysis algorithm for blended speech is proposed; it can detect the existence and estimate the embedding rate of blended speech. First, the odd-even difference (OED) of the speech signal is calculated and divided into frames. The average zero crossing rate (ZCR) is calculated for each OED frame, and the minimum average ZCR and AZCR-OED of the entire speech signal are extracted as features. Then, a support vector machine classifier is used to determine whether the speech signal is blended. Finally, a voice activity detection algorithm is applied to determine the hidden location of the secret speech and estimate the embedding rate. The results demonstrate that without attack, the detection accuracy can reach 80 % or more when the embedding rate is greater than 10 %, and the estimated embedding rate is similar to the real value. And when some attacks occur, it can also reach relatively high detection accuracy. The algorithm has high performance in terms of accuracy, effectiveness and robustness.

No MeSH data available.


Detection accuracy under different types of attacks
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Fig7: Detection accuracy under different types of attacks

Mentions: We extracted the feature parameters from the attacked training speeches, and sent them to trained SVM classifier respectively. Then we can detect the existence of secret speech. Figure 7 shows the experimental results (for a better comparison, we redraw the curve in Fig. 7).Fig. 7


Existence detection and embedding rate estimation of blended speech in covert speech communications.

Li L, Gao Y - Springerplus (2016)

Detection accuracy under different types of attacks
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4940342&req=5

Fig7: Detection accuracy under different types of attacks
Mentions: We extracted the feature parameters from the attacked training speeches, and sent them to trained SVM classifier respectively. Then we can detect the existence of secret speech. Figure 7 shows the experimental results (for a better comparison, we redraw the curve in Fig. 7).Fig. 7

Bottom Line: The average zero crossing rate (ZCR) is calculated for each OED frame, and the minimum average ZCR and AZCR-OED of the entire speech signal are extracted as features.The results demonstrate that without attack, the detection accuracy can reach 80 % or more when the embedding rate is greater than 10 %, and the estimated embedding rate is similar to the real value.And when some attacks occur, it can also reach relatively high detection accuracy.

View Article: PubMed Central - PubMed

Affiliation: College of Electronics and Information Engineering, Sichuan University, Chengdu, 610064 Sichuan China.

ABSTRACT
Covert speech communications may be used by terrorists to commit crimes through Internet. Steganalysis aims to detect secret information in covert communications to prevent crimes. Herein, based on the average zero crossing rate of the odd-even difference (AZCR-OED), a steganalysis algorithm for blended speech is proposed; it can detect the existence and estimate the embedding rate of blended speech. First, the odd-even difference (OED) of the speech signal is calculated and divided into frames. The average zero crossing rate (ZCR) is calculated for each OED frame, and the minimum average ZCR and AZCR-OED of the entire speech signal are extracted as features. Then, a support vector machine classifier is used to determine whether the speech signal is blended. Finally, a voice activity detection algorithm is applied to determine the hidden location of the secret speech and estimate the embedding rate. The results demonstrate that without attack, the detection accuracy can reach 80 % or more when the embedding rate is greater than 10 %, and the estimated embedding rate is similar to the real value. And when some attacks occur, it can also reach relatively high detection accuracy. The algorithm has high performance in terms of accuracy, effectiveness and robustness.

No MeSH data available.