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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.


Hidden location of the secret speech: a Actual hidden location and b Estimated hidden location
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Fig5: Hidden location of the secret speech: a Actual hidden location and b Estimated hidden location

Mentions: We chose two speech samples randomly from the VOASE library. Then, we chose one to be the secret speech and hid it in the other using the blending-based speech hiding algorithm with an embedding rate of 50 % and a hidden degree factor . Then, the algorithm presented above was used to detect the hidden location of the secret speech. A frame length of 256 was used. The result is shown in Fig. 5.Fig. 5


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

Li L, Gao Y - Springerplus (2016)

Hidden location of the secret speech: a Actual hidden location and b Estimated hidden location
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig5: Hidden location of the secret speech: a Actual hidden location and b Estimated hidden location
Mentions: We chose two speech samples randomly from the VOASE library. Then, we chose one to be the secret speech and hid it in the other using the blending-based speech hiding algorithm with an embedding rate of 50 % and a hidden degree factor . Then, the algorithm presented above was used to detect the hidden location of the secret speech. A frame length of 256 was used. The result is shown in Fig. 5.Fig. 5

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.