Universal audio steganalysis based on calibration and reversed frequency resolution of human auditory system

Abstract

Calibration and higher order statistics (HOS) are standard components of image steganalysis. However, these techniques have not yet found adequate attention in audio steganalysis. Specifically, most of current studies are either non-calibrated or only based on noise removal. The goal of this paper is to fill these gaps and to show that calibrated features based on re-embedding technique improves performance of audio steganalysis. Furthermore, we show that least significant bit (LSB) is the most sensitive bit-plane to data hiding algorithms and therefore it can be employed as a universal embedding method. The proposed features also benefit from an efficient model which is tailored to the needs for audio steganalysis and represent the maximum deviation from human auditory system (HAS). Performance of the proposed method is evaluated on a wide range of data hiding algorithms in both targeted and universal paradigms. The results show the effectiveness of the proposed method in detecting the finest traces of data hiding algorithms in very low embedding rates. The system detects steghide at capacity of 0.06 bit per symbol (BPS) with sensitivity of 98.6% (music) and 78.5% (speech). These figures are respectively 7.1% and 27.5% higher than the state-of-the-art results based on RMFCC features. 1-the intended message ( ∈ ℳ) inside a host signal, namely called cover ( ∈ ). Steganography methods can be classified into categories of text, audio, image, video, and network traffics, depending on the type of cover signal.Steganalysis is the countermeasure of steganography which aims to detect the presence of hidden messages. Likewise, steganalysis methods may be classified according to the type of cover into categories of text, audio, image, video, and network traffics. Steganalysis in each of these categories can be further divided into targeted and universal methods. In the former, the embedding algorithm is known, whereas there is no prior assumption about the embedding algorithm in the later one [6].One of the first audio steganalysis method was proposed in [7] where cover signal was estimated by de-noising the signal under inspection. Audio quality metrics (AQMs) were used to quantify the discrepancies between the original signal and its estimated cover [7]. Hausdroff distance was proposed as a solution to the inefficiency of AQMs in detecting traces of hidden data [8]. In [9], negative effect of high correlation between the features extracted from these denoising methods and their signals was solved.All of these previous works are similar in that, they have used indirect methods for comparing between stegos and their estimated covers. However, conducting this comparison on the distributions of stegos and covers are more appropriate. This approach was pursued in [10], where it was shown that the degree of histograms flatness derived from wavelet coefficients of stegos and their cover counterparts is a discriminative criterion. Gaussian mixture model (GMM) and generalized Gaussian distribution (GGD) were used to capture this...

Robust Speech Steganography Using Differential SVD

“…The steganographic security (statistical undetectability) is the import criteria to evaluate the steganographic systems. In the experiments, four steganalysis methods [29], [30] are used to test the security of the proposed method. Accordingly, four kinds of features are extracted for steganalysis, which include derivative-based high-frequency spectrum (DHS), derivative-based mel-cepstrum (DMC), wavelet-based melcepstrum (WMC) and reversed-Mel energy (RME).…”

Comprehensive review of audio steganalysis methods

“…Previous calibration methods are similar in the sense that estimation of covers was used for calibration. Calibrating features based on the characteristics of stegos is another possibility that was pursued in [28]. That paper addressed both targeted and universal cases.…”

“…Such a method may make a decision about every frame and then use an appropriate rule for making the final decision. Also, such methods would be very beneficial for steganography and its results can be used for better understanding the cover space and implement adaptive steganography methods. Previous works have shown the effect of audio contents on the result of steganalysis [11, 28]. Therefore, implementing a proper clustering method before steganalysis and investigating its effect seems to be fruitful. Current literature has only considered steganalysis of mono signals.…”

Image Steganography Based on Artificial Immune in Mobile Edge Computing With Internet of Things

“…The transform domain algorithms embed secret by modifying the transform domain coefficients, that are, Discrete Cosine Transform (DCT) [20], variants of Discrete Wavelet Transform (DWT) [21], and Fast Fourier Transform (FFT) [22]. Many state-of-the-art transform domain algorithms have emerged, such as Jsteg [23], OutGuess [24], StegHide [25], F5 [26], and so on. These algorithms preserve the image statistical characteristics as much as possible during the embedding process to defense the visual-based and statistical-based steganalysis attacks.…”