<title>Steganalysis of audio based on audio quality metrics</title>

Abstract

Classification of audio documents as bearing hidden information or not is a security issue addressed in the context of steganalysis. A cover audio object can be converted into a stego-audio object via steganographic methods. In this study we present a statistical method to detect the presence of hidden messages in audio signals. The basic idea is that, the distribution of various statistical distance measures, calculated on cover audio signals and on stego-audio signals vis-à-vis their denoised versions, are statistically different. The design of audio steganalyzer relies on the choice of these audio quality measures and the construction of a two-class classifier. Experimental results show that the proposed technique can be used to detect the presence of hidden messages in digital audio data.

A Novel Audio Steganalysis Based on High-Order Statistics of a Distortion Measure with Hausdorff Distance

“…However, it is important to note that, in a real communication environment, a reference audio file needs to be used since we cannot get specific information about the original coveraudio object. The de-noised version of an audio file has already been shown to be a good estimation of the cover signal [6]. Note that the de-noised version of stego-audio is still the de-noised cover-audio with some i.i.d.…”

“…Specifically, some image quality metrics, e.g., Minkowsky [18], correlation, and human visual system (HVS) based measures [19] [20], are selected as the feature set to distinguish between coverimages and stego-images. This concept was extended to audio steganalysis in [6]. Similar to [18], the potential of distortion audio metrics is used to build a steganalyzer to discriminate between cover-audio objects and stego-audio objects.…”

“…However, since the data-embedding method is typically unknown prior to detection, we focus on the design of a unified steganalysis algorithm to detect the presence of steganography independent of the steganography algorithms used. Moreover, we focus on passive detection as opposed to active warden steganalysis [6] which aim to detect and modify the hidden content.…”

“…High order statistics derived from this distortion measure can then be used to generate features for a classifier. Unlike previous work in audio steganalysis that used the traditional audio quality metrics, such as signal-to-noise ratio (SNR), Perceptual Audio Quality Measure (PAQM) [6], and other such metrics, the proposed distortion measure is designed specifically to detect modifications to pure audio content as follows.…”

“…Johnson et al proposed a universal steganalysis algorithm that exploits the statistical regularities of recorded speech [5]. In [6], audio quality metrics were adopted to capture the distortion introduced by the hidden information. Later in [7], Avcibas proposed an audio steganalysis algorithm using content-independent distortion measures.…”