Abstract
Steganography in inactive Voice-over-IP frames is a new technique of information hiding, which can achieve large steganographic capacity while maintaining excellent imperceptibility. To prevent the illegitimate use of this technique, the entropy-based and poker test-based steganalysis methods have been presented. However, the detection performance of these two methods is not so good for the cases of having small quantity of inactive frames or low embedding rates. Thus, we present a new steganalysis method based on statistic characteristics of fundamental frequency. Specifically, we employ the statistics for zero-crossing count (ZCC), including the average ZCC of inactive frames, the ratio between the average ZCC of inactive frames and that of all frames, and the difference between the average ZCC of inactive frames and their calibrated versions, to characterize the frame-level dynamic characteristic of speech signals; we utilize the average values of Mel-frequency cepstral coefficients (MFCCs) to represent the invariant characteristic of inactive frames; further, using the feature set consisting of the zero-crossing statistics and average MFCCs, we propose a support-vector-machine based steganalysis for inactive speech frames. The proposed steganalysis method is evaluated with a large number of ITU-T G.723.1 encoded speech samples, and compared with the existing methods. The experimental results demonstrate that the proposed method significantly outperforms the previous ones on detection accuracy, false positive rate and false negative rate for any given embedding rates or using the same number of inactive frames. Particularly, the proposed method can provide accurate detecting results for the existing steganographic methods only using very small quantity of inactive frames, and thereby be employed to detecting potential inactive-frame steganography behaviors in real-time speech streams.
Highlights
Steganography is a technique of covert communication by hiding information into digital media without causing anyThe associate editor coordinating the review of this manuscript and approving it for publication was Xiaochun Cheng .perceptible distortion
To detect steganography for fixed codebook (FCB) parameters in adaptive multi-rate (AMR) speech streams, Tian et al [14] presented a support-vector-machine (SVM) based steganalysis method using three kinds of statistical features for pulse pairs, namely, long-term distribution features based on the probability distributions of pulse pairs, shortterm invariant features based on Markov transition probabilities of pulse pairs, and track-to-track correlation features based on the joint probability matrices of pulse pairs
Fundamental frequency estimation is popularly applied in the field of speech signal processing [43]–[46], in voice activity detection [45], [46]. Inspired by these successful applications, we study the impact of steganography in inactive frames on fundamental frequency characteristics, and find out that the statistics for zerocrossing count and Mel-frequency cepstral coefficients are eminently suitable for discriminating the cover and steganographic speech samples
Summary
Steganography is a technique of covert communication by hiding information into digital media (such as image [1], video [2], audio [3] and text [4]) without causing any. To detect steganography for FCB parameters in AMR speech streams, Tian et al [14] presented a support-vector-machine (SVM) based steganalysis method using three kinds of statistical features for pulse pairs, namely, long-term distribution features based on the probability distributions of pulse pairs, shortterm invariant features based on Markov transition probabilities of pulse pairs, and track-to-track correlation features based on the joint probability matrices of pulse pairs They proposed a feature selection mechanism based on adaptive boosting to optimize the feature set as well as reduce its dimension.
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