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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.


OEDs of the pure speech and the blended speech: a, b, c, and d
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Fig1: OEDs of the pure speech and the blended speech: a, b, c, and d

Mentions: We selected two pure speech samples from the Voice of America (VOA) Special English corpus (http://www.51voa.com/VOA_Special_English/) randomly and transformed them into the WAV format with an 8-kHz sampling rate and 16-bit speech encoding. Then, one of the samples was chosen to be the secret speech and hidden in the other using the blending-based speech hiding algorithm, with an embedding rate of 100 % and a hidden degree factor . Finally, both the pure and blended speech were transmitted through QQ, a popular instant messaging software service in China that is used for chatting, and the receiver analyzed the OED of the received speech. The result is shown in Fig. 1 (to see the result clearly, only data points 1–200 are shown in the figure).Fig. 1


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

Li L, Gao Y - Springerplus (2016)

OEDs of the pure speech and the blended speech: a, b, c, and d
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig1: OEDs of the pure speech and the blended speech: a, b, c, and d
Mentions: We selected two pure speech samples from the Voice of America (VOA) Special English corpus (http://www.51voa.com/VOA_Special_English/) randomly and transformed them into the WAV format with an 8-kHz sampling rate and 16-bit speech encoding. Then, one of the samples was chosen to be the secret speech and hidden in the other using the blending-based speech hiding algorithm, with an embedding rate of 100 % and a hidden degree factor . Finally, both the pure and blended speech were transmitted through QQ, a popular instant messaging software service in China that is used for chatting, and the receiver analyzed the OED of the received speech. The result is shown in Fig. 1 (to see the result clearly, only data points 1–200 are shown in the figure).Fig. 1

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.