Mel-cepstrum-based steganalysis for VoIP steganography
01 Mar 2007-Vol. 6505, pp 650505
TL;DR: In this article, a Mel-cepstrum-based analysis known from speaker and speech recognition is used to perform a detection of embedded hidden messages in VoIP applications, which can detect information hiding in the field of hidden communication as well as for DRM applications.
Abstract: Steganography and steganalysis in VoIP applications are important research topics as speech data is an appropriate
cover to hide messages or comprehensive documents. In our paper we introduce a Mel-cepstrum based
analysis known from speaker and speech recognition to perform a detection of embedded hidden messages. In
particular we combine known and established audio steganalysis features with the features derived from Melcepstrum
based analysis for an investigation on the improvement of the detection performance. Our main focus
considers the application environment of VoIP-steganography scenarios.
The evaluation of the enhanced feature space is performed for classical steganographic as well as for watermarking
algorithms. With this strategy we show how general forensic approaches can detect information hiding
techniques in the field of hidden communication as well as for DRM applications. For the later the detection of
the presence of a potential watermark in a specific feature space can lead to new attacks or to a better design of
the watermarking pattern. Following that the usefulness of Mel-cepstrum domain based features for detection is
discussed in detail.
Citations
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20 Sep 2007
TL;DR: The results show, that for the test set, the used classification techniques and selected steganalysis features, microphones can be better classified than environments.
Abstract: In this paper a first approach for digital media forensics is presented to determine the used microphones and the environments of recorded digital audio samples by using known audio steganalysis features. Our first evaluation is based on a limited exemplary test set of 10 different audio reference signals recorded as mono audio data by four microphones in 10 different rooms with 44.1 kHz sampling rate and 16 bit quantisation. Note that, of course, a generalisation of the results cannot be achieved. Motivated by the syntactical and semantical analysis of information and in particular by known audio steganalysis approaches, a first set of specific features are selected for classification to evaluate, whether this first feature set can support correct classifications. The idea was mainly driven by the existing steganalysis features and the question of applicability within a first and limited test set. In the tests presented in this paper, an inter-device analysis with different device characteristics is performed while intra-device evaluations (identical microphone models of the same manufacturer) are not considered. For classification the data mining tool WEKA with K-means as a clustering and Naive Bayes as a classification technique are applied with the goal to evaluate their classification in regard to the classification accuracy on known audio steganalysis features. Our results show, that for our test set, the used classification techniques and selected steganalysis features, microphones can be better classified than environments. These first tests show promising results but of course are based on a limited test and training set as well a specific test set generation. Therefore additional and enhanced features with different test set generation strategies are necessary to generalise the findings.
154 citations
TL;DR: Experimental results show that proposed derivative-based and wavelet-based approaches remarkably improve the detection accuracy.
Abstract: To improve a recently developed mel-cepstrum audio steganalysis method, we present in this paper a method based on Fourier spectrum statistics and mel-cepstrum coefficients, derived from the second-order derivative of the audio signal. Specifically, the statistics of the high-frequency spectrum and the mel-cepstrum coefficients of the second-order derivative are extracted for use in detecting audio steganography. We also design a wavelet-based spectrum and mel-cepstrum audio steganalysis. By applying support vector machines to these features, unadulterated carrier signals (without hidden data) and the steganograms (carrying covert data) are successfully discriminated. Experimental results show that proposed derivative-based and wavelet-based approaches remarkably improve the detection accuracy. Between the two new methods, the derivative-based approach generally delivers a better performance.
126 citations
TL;DR: A survey of the existing VoIP steganography methods and their countermeasures can be found in this article, where the authors present a first survey of these methods and countermeasures.
Abstract: Steganography is an ancient art that encompasses various techniques of information hiding, the aim of which is to embed secret information into a carrier message. Steganographic methods are usually aimed at hiding the very existence of the communication. Due to the rise in popularity of IP telephony, together with the large volume of data and variety of protocols involved, it is currently attracting the attention of the research community as a perfect carrier for steganographic purposes. This article is a first survey of the existing Voice over IP (VoIP) steganography methods and their countermeasures.
98 citations
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TL;DR: This article is a first survey of the existing Voice over IP (VoIP) steganography methods and their countermeasures.
Abstract: Steganography is an ancient art that encompasses various techniques of information hiding, the aim of which is to secret information into a carrier message. Steganographic methods are usually aimed at hiding the very existence of the communication. Due to the rise in popularity of IP telephony, together with the large volume of data and variety of protocols involved, it is currently attracting the attention of the research community as a perfect carrier for steganographic purposes. This paper is a survey of the existing VoIP steganography (steganophony) methods and their countermeasures.
70 citations
TL;DR: This paper proposes an effective online steganalysis method to detect QIM steganography and finds four strong codeword correlation patterns in VoIP streams, which will be distorted after embedding with hidden data.
Abstract: Quantization index modulation (QIM) steganography makes it possible to hide secret information in voice-over IP (VoIP) streams, which could be utilized by unauthorized entities to set up covert channels for malicious purposes. Detecting short QIM steganography samples, as is required by real circumstances, remains an unsolved challenge. In this paper, we propose an effective online steganalysis method to detect QIM steganography. We find four strong codeword correlation patterns in VoIP streams, which will be distorted after embedding with hidden data. To extract those correlation features, we propose the codeword correlation model, which is based on recurrent neural network (RNN). Furthermore, we propose the feature classification model to classify those correlation features into cover speech and stego speech categories. The whole RNN-based steganalysis model (RNN-SM) is trained in a supervised learning framework. Experiments show that on full embedding rate samples, RNN-SM is of high detection accuracy, which remains over 90% even when the sample is as short as 0.1 s, and is significantly higher than other state-of-the-art methods. For the challenging task of conducting steganalysis towards low embedding rate samples, RNN-SM also achieves a high accuracy. The average testing time for each sample is below 0.15% of sample length. These clues show that RNN-SM meets the short sample detection demand and is a state-of-the-art algorithm for online VoIP steganalysis.
68 citations
References
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TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
Abstract: LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
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01 Jan 1995TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Abstract: Setting of the learning problem consistency of learning processes bounds on the rate of convergence of learning processes controlling the generalization ability of learning processes constructing learning algorithms what is important in learning theory?.
40,147 citations
01 Nov 1977
TL;DR: The effects of modifications made to the short-time transform are explicitly shown on the resulting signal and it is shown that a formal duality exists between the two synthesis methods based on the properties of the window used for obtaining theshort-time Fourier transform.
Abstract: Two distinct methods for synthesizing a signal from its short-time Fourier transform have previously been proposed. We call these methods the filter-bank summation (FBS) method and the overlap add (OLA) method. Each of these synthesis techniques has unique advantages and disadvantages in various applications due to the way in which the signal is reconstructed. In this paper we unify the ideas behind the two synthesis techniques and discuss the similarities and differences between these methods. In particular, we explicitly show the effects of modifications made to the short-time transform (both fixed and time-varying modifications are considered) on the resulting signal and discuss applications where each of the techniques would be most useful The interesting case of nonlinear modifications (possibly signal dependent) to the short-time Fourier transform is also discussed. Finally it is shown that a formal duality exists between the two synthesis methods based on the properties of the window used for obtaining the short-time Fourier transform.
954 citations
07 Oct 2002
TL;DR: In this article, a wavelet-like decomposition is used to build higher-order statistical models of natural images and support vector machines are then used to discriminate between untouched and adulterated images.
Abstract: Techniques for information hiding have become increasingly more sophisticated and widespread. With high-resolution digital images as carriers, detecting hidden messages has become considerably more difficult. This paper describes an approach to detecting hidden messages in images that uses a wavelet-like decomposition to build higher-order statistical models of natural images. Support vector machines are then used to discriminate between untouched and adulterated images.
529 citations