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.
Highlights
Covert audio communication refers to the technique of embedding secret information into carrier audio such that important information can be transmitted safely and reliably via public communication
This paper proposes a steganalysis algorithm for blended speech transmitted via a high-quality Internet
Ten copies of the other 500 speech files were made to be used as carrier speech files, which were embedded with secret speech using the blending-based speech hiding algorithm with 10 different embedding rates of 10, 20, 30... 100 %
Summary
Covert audio communication refers to the technique of embedding secret information into carrier audio such that important information can be transmitted safely and reliably via public communication. Li and Gao SpringerPlus (2016)5:1049 et al 2005; Tatsuya and Kotaro 2015; Chen 2001) include the following: least significant bits (LSB) hiding (Tayel et al 2016; Hartoko et al 2015; Krishnan and Abdullah 2016), phase coding hiding (Nutzinger and Juergen 2011), direct sequence spread spectrum (DSSS) encoding hiding (Matsuoka 2006), echo hiding (Byeong-Seob et al 2005; Tatsuya and Kotaro 2015), and blending-based speech hiding (Chen 2001), along with others Among these algorithms, the blending-based speech hiding algorithm is different from others, where the secret speech can be hidden directly in the carrier speech and does not need to be binary encoded. This algorithm has a good robustness (Rangding et al 2004) and high hidden capacity
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