Steganalysis of Inactive Voice-Over-IP Frames Based on Poker Test

Abstract

This paper concentrates on the detection of steganography in inactive frames of low bit rate audio streams in Voice over Internet Protocol (VoIP) scenarios. Both theoretical and experimental analyses demonstrate that the distribution of 0 and 1 in encoding parameter bits becomes symmetric after a steganographic process. Moreover, this symmetry affects the frequency of each subsequence of parameter bits, and accordingly changes the poker test statistical features of encoding parameter bits. Employing the poker test statistics of each type of encoding parameter bits as detection features, we present a steganalysis method based on a support vector machine. We evaluate the proposed method with a large quantity of speech samples encoded by G.723.1 and compare it with the entropy test. The experimental results show that the proposed method is effective, and largely outperforms the entropy test in any cases.

An Approach for Stego-Insider Detection Based on a Hybrid NoSQL Database

“…Steganalysis [33][34][35] is very popular for detecting hidden messages in individual digitized objects. In addition to the very fact of transmission, it is able (in some cases) to determine the message itself, the methods used, to decrypt it, etc.…”

Detecting Steganography in Inactive Voice-Over-IP Frames Based on Statistic Characteristics of Fundamental Frequency

“…In this section, we evaluate the performance of the proposed method, and compare it with the entropy-based and poker test-based methods [35]. In the steganalysis experiments, all the SVM-based classifiers are implemented based on LibSVM [53] with RBF kernel, where the default parameter setting is adopted.…”

“…As for the steganalysis of VoIP, there have been also many fruitful studies [13]- [15], [29]- [35]. For example, Lin et al [15] introduced a recurrent neural network to detect quantization index modulation-based steganography for LPC parameters in G.729a speech streams, which can achieve excellent detection performance, even for very short speech samples, and significantly outperforms the steganalysis based on quantization codeword correlation network [29].…”

“…By contrast, the steganalysis for inactive speech frames is largely unexplored. Recently, two classical statistics, i.e., entropy and poker test statistic, were employed as the steganalysis features to detect steganography in inactive speech frames [35]. The experimental results show that these two methods are feasible, while the latter is better than the former.…”

“…Further, using the feature set consisting of the zero-crossing statistics and average values of MFCCs, an SVM-based steganalysis for inactive speech frames is presented. The proposed method is evaluated with a large number of ITU-T G.723.1 encoded speech samples, and compared with entropy-based [38] and poker test-based [35] methods. The experimental results demonstrate that the proposed method significantly outperforms the previous ones in detection accuracy, false positive rate and false negative rate for any given embedding rates or using the same quantity of inactive frames.…”