Faked Speech Detection with Zero Knowledge

Audio is one of the most used ways of human communication, but at the same time it can be easily misused to trick people. With the revolution of AI, the related technologies are now accessible to almost everyone thus making it simple for the criminals to commit crimes and forgeries. In this work, we introduce a neural network method to develop a classifier that will blindly classify an input audio as real or mimicked; the word 'blindly' refers to the ability to detect mimicked audio without references or real sources. The proposed model was trained on a set of important features extracted from a large dataset of audios to get a classifier that was tested on the same set of features from different audios. The data was extracted from two raw datasets, especially composed for this work; an all English dataset and a mixed dataset (Arabic plus English). These datasets have been made available, in raw form, through GitHub for the use of the research community at https://github.com/SaSs7/Dataset. For the purpose of comparison, the audios were also classified through human inspection with the subjects being the native speakers. The ensued results were interesting and exhibited formidable accuracy.Comment: 14 pages, 4 figures (6 if you count subfigures), 2 table

Multibiometric security in wireless communication systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 05/08/2010.This thesis has aimed to explore an application of Multibiometrics to secured wireless communications. The medium of study for this purpose included Wi-Fi, 3G, and WiMAX, over which simulations and experimental studies were carried out to assess the performance. In specific, restriction of access to authorized users only is provided by a technique referred to hereafter as multibiometric cryptosystem. In brief, the system is built upon a complete challenge/response methodology in order to obtain a high level of security on the basis of user identification by fingerprint and further confirmation by verification of the user through text-dependent speaker recognition. First is the enrolment phase by which the database of watermarked fingerprints with memorable texts along with the voice features, based on the same texts, is created by sending them to the server through wireless channel. Later is the verification stage at which claimed users, ones who claim are genuine, are verified against the database, and it consists of five steps. Initially faced by the identification level, one is asked to first present one’s fingerprint and a memorable word, former is watermarked into latter, in order for system to authenticate the fingerprint and verify the validity of it by retrieving the challenge for accepted user. The following three steps then involve speaker recognition including the user responding to the challenge by text-dependent voice, server authenticating the response, and finally server accepting/rejecting the user. In order to implement fingerprint watermarking, i.e. incorporating the memorable word as a watermark message into the fingerprint image, an algorithm of five steps has been developed. The first three novel steps having to do with the fingerprint image enhancement (CLAHE with 'Clip Limit', standard deviation analysis and sliding neighborhood) have been followed with further two steps for embedding, and extracting the watermark into the enhanced fingerprint image utilising Discrete Wavelet Transform (DWT). In the speaker recognition stage, the limitations of this technique in wireless communication have been addressed by sending voice feature (cepstral coefficients) instead of raw sample. This scheme is to reap the advantages of reducing the transmission time and dependency of the data on communication channel, together with no loss of packet. Finally, the obtained results have verified the claims

Performance and security analysis of Gait-based user authentication

Verifying the identity of a user, usually referred to as user authentication, before granting access to the services or objects is a very important step in many applications. People pass through some sorts of authentication process in their daily life. For example, to prove having access to the computer the user is required to know a password. Similarly, to be able to activate a mobile phone the owner has to know its PIN code, etc. Some user authentication techniques are based on human physiological or behavioral characteristics such as fingerprints, face, iris and so on. Authentication methods differ in their advantages and disadvantages, e.g. PIN codes and passwords have to be remembered, eye-glasses must be taken off for face authentication, etc. Security and usability are important aspects of user authentication. The usability aspect relates to the unobtrusiveness, convenience and user-friendliness of the authentication technique. Security is related to the robustness of the authentication method against attacks. Recent advances in electronic chip development offer new opportunities for person authentication based on his gait (walking style) using small, light and cheap sensors. One of the primary advantages of this approach is that it enables unobtrusive user authentication. Although studies on human recognition based on gait indicate encouraging performances, the security per se (i.e. robustness and/or vulnerability) of gait-based recognition systems has received little or no attention. The overall goal of the work presented in this thesis is on performance and security analysis of gait-based user authentication. The nature of the contributions is not on developing novel algorithms, but rather on enhancing existing approaches in gait-based recognition using small and wearable sensors, and developing new knowledge on security and uniqueness of gait. The three main research questions addressed in this thesis are: (1) What are the performances of recognition methods that are based on the motion of particular body parts during gait? (2) How robust is the gait-based user authentication? (3) What aspects do influence the uniqueness of human gait? In respect to the first research question, the thesis identifies several locations on the body of the person, whose motion during gait can provide identity information. These body parts include hip, trouser pockets, arm and ankle. Analysis of acceleration signals indicates that movements of these body segments have some discriminative power. This might make these modalities suitable as an additional factor in multi-factor authentication. For the research question on security as far as we know, this thesis is the first extensive analysis of gait authentication security (in case of hip motion). A gait-based authentication system is studied under three attack scenarios. These attack scenarios include a minimal effort-mimicry (with restricted time and number of attempts), knowing the closest person in the database (in terms of gait similarity) and knowing the gender of the user in the database. The findings of the thesis reveal that the minimal effort mimicking does not help to improve the acceptance chances of impostors. However, impostors who know their closest person in the database or the genders of the users in the database can be a threat to gait-based authentication systems. In the third research question, the thesis provides some insights towards understanding the uniqueness of gait in case of ankle/foot motion. In particular, it reveals the following: heavy footwear tends to diminish foot discriminativeness; a sideway motion of the foot provides the most discrimination, compared to an up-down or forward-backward direction of the motion; and different parts of the gait cycle provide different level of discrimination. In addition, the thesis proposes taxonomy of user recognition methods based on gait. In addition, the thesis work has also resulted in the follwoing paper which is closely related or overlapping with papers mentioned below. Davrondzhon Gafurov, Kirsi Helkala and Torkjel Søndrol, Biometric Gait Authentication Using Accelerometer Sensor, Journal of Computers, 1(7), pp.51-59, 2006: http://www.academypublisher.com/jcp/vol01/no07/jcp01075159.pdf List of papers. The 8 research papers that constitute the main research part of the thesis are

Robust speaker recognition using both vocal source and vocal tract features estimated from noisy input utterances.

Wang, Ning.Thesis (M.Phil.)--Chinese University of Hong Kong, 2007.Includes bibliographical references (leaves 106-115).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Introduction to Speech and Speaker Recognition --- p.1Chapter 1.2 --- Difficulties and Challenges of Speaker Authentication --- p.6Chapter 1.3 --- Objectives and Thesis Outline --- p.7Chapter 2 --- Speaker Recognition System --- p.10Chapter 2.1 --- Baseline Speaker Recognition System Overview --- p.10Chapter 2.1.1 --- Feature Extraction --- p.12Chapter 2.1.2 --- Pattern Generation and Classification --- p.24Chapter 2.2 --- Performance Evaluation Metric for Different Speaker Recognition Tasks --- p.30Chapter 2.3 --- Robustness of Speaker Recognition System --- p.30Chapter 2.3.1 --- Speech Corpus: CU2C --- p.30Chapter 2.3.2 --- Noise Database: NOISEX-92 --- p.34Chapter 2.3.3 --- Mismatched Training and Testing Conditions --- p.35Chapter 2.4 --- Summary --- p.37Chapter 3 --- Speaker Recognition System using both Vocal Tract and Vocal Source Features --- p.38Chapter 3.1 --- Speech Production Mechanism --- p.39Chapter 3.1.1 --- Speech Production: An Overview --- p.39Chapter 3.1.2 --- Acoustic Properties of Human Speech --- p.40Chapter 3.2 --- Source-filter Model and Linear Predictive Analysis --- p.44Chapter 3.2.1 --- Source-filter Speech Model --- p.44Chapter 3.2.2 --- Linear Predictive Analysis for Speech Signal --- p.46Chapter 3.3 --- Vocal Tract Features --- p.51Chapter 3.4 --- Vocal Source Features --- p.52Chapter 3.4.1 --- Source Related Features: An Overview --- p.52Chapter 3.4.2 --- Source Related Features: Technical Viewpoints --- p.54Chapter 3.5 --- Effects of Noises on Speech Properties --- p.55Chapter 3.6 --- Summary --- p.61Chapter 4 --- Estimation of Robust Acoustic Features for Speaker Discrimination --- p.62Chapter 4.1 --- Robust Speech Techniques --- p.63Chapter 4.1.1 --- Noise Resilience --- p.64Chapter 4.1.2 --- Speech Enhancement --- p.64Chapter 4.2 --- Spectral Subtractive-Type Preprocessing --- p.65Chapter 4.2.1 --- Noise Estimation --- p.66Chapter 4.2.2 --- Spectral Subtraction Algorithm --- p.66Chapter 4.3 --- LP Analysis of Noisy Speech --- p.67Chapter 4.3.1 --- LP Inverse Filtering: Whitening Process --- p.68Chapter 4.3.2 --- Magnitude Response of All-pole Filter in Noisy Condition --- p.70Chapter 4.3.3 --- Noise Spectral Reshaping --- p.72Chapter 4.4 --- Distinctive Vocal Tract and Vocal Source Feature Extraction . . --- p.73Chapter 4.4.1 --- Vocal Tract Feature Extraction --- p.73Chapter 4.4.2 --- Source Feature Generation Procedure --- p.75Chapter 4.4.3 --- Subband-specific Parameterization Method --- p.79Chapter 4.5 --- Summary --- p.87Chapter 5 --- Speaker Recognition Tasks & Performance Evaluation --- p.88Chapter 5.1 --- Speaker Recognition Experimental Setup --- p.89Chapter 5.1.1 --- Task Description --- p.89Chapter 5.1.2 --- Baseline Experiments --- p.90Chapter 5.1.3 --- Identification and Verification Results --- p.91Chapter 5.2 --- Speaker Recognition using Source-tract Features --- p.92Chapter 5.2.1 --- Source Feature Selection --- p.92Chapter 5.2.2 --- Source-tract Feature Fusion --- p.94Chapter 5.2.3 --- Identification and Verification Results --- p.95Chapter 5.3 --- Performance Analysis --- p.98Chapter 6 --- Conclusion --- p.102Chapter 6.1 --- Discussion and Conclusion --- p.102Chapter 6.2 --- Suggestion of Future Work --- p.10

Effects of Equipment Variations on Speaker Recognition Error Rates

The purpose of this study was to examine the effects that equipment variation has on speaker recognition performance. Specifically microphone variation is investigated. The study examines the error rates of a speaker recognition system when microphones vary between the enrollment and testing phases. The study also examines the error rates of a speaker recognition system when microphones differ in similar environments and conditions. The metric for evaluation of effect is the false identity acceptance and the false identity rejection error rates.School of Electrical & Computer Engineerin

Robust speaker identification against computer aided voice impersonation

Speaker Identification (SID) systems offer good performance in the case of noise free speech and most of the on-going research aims at improving their reliability in noisy environments. In ideal operating conditions very low identification error rates can be achieved. The low error rates suggest that SID systems can be used in real-life applications as an extra layer of security along with existing secure layers. They can, for instance, be used alongside a Personal Identification Number (PIN) or passwords. SID systems can also be used by law enforcements agencies as a detection system to track wanted people over voice communications networks. In this thesis, the performance of 'the existing SID systems against impersonation attacks is analysed and strategies to counteract them are discussed. A voice impersonation system is developed using Gaussian Mixture Modelling (GMM) utilizing Line Spectral Frequencies (LSF) as the features representing the spectral parameters of the source-target pair. Voice conversion systems based on probabilistic approaches suffer from the problem of over smoothing of the converted spectrum. A hybrid scheme using Linear Multivariate Regression and GMM, together with posterior probability smoothing is proposed to reduce over smoothing and alleviate the discontinuities in the converted speech. The converted voices are used to intrude a closed-set SID system in the scenarios of identity disguise and targeted speaker impersonation. The results of the intrusion suggest that in their present form the SID systems are vulnerable to deliberate voice conversion attacks. For impostors to transform their voices, a large volume of speech data is required, which may not be easily accessible. In the context of improving the performance of SID against deliberate impersonation attacks, the use of multiple classifiers is explored. Linear Prediction (LP) residual of the speech signal is also analysed for speaker-specific excitation information. A speaker identification system based on multiple classifier system, using features to describe the vocal tract and the LP residual is targeted by the impersonation system. The identification results provide an improvement in rejecting impostor claims when presented with converted voices. It is hoped that the findings in this thesis, can lead to the development of speaker identification systems which are better equipped to deal with the problem with deliberate voice impersonation.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Multibiometric security in wireless communication systems

This thesis has aimed to explore an application of Multibiometrics to secured wireless communications. The medium of study for this purpose included Wi-Fi, 3G, and WiMAX, over which simulations and experimental studies were carried out to assess the performance. In specific, restriction of access to authorized users only is provided by a technique referred to hereafter as multibiometric cryptosystem. In brief, the system is built upon a complete challenge/response methodology in order to obtain a high level of security on the basis of user identification by fingerprint and further confirmation by verification of the user through text-dependent speaker recognition. First is the enrolment phase by which the database of watermarked fingerprints with memorable texts along with the voice features, based on the same texts, is created by sending them to the server through wireless channel. Later is the verification stage at which claimed users, ones who claim are genuine, are verified against the database, and it consists of five steps. Initially faced by the identification level, one is asked to first present one’s fingerprint and a memorable word, former is watermarked into latter, in order for system to authenticate the fingerprint and verify the validity of it by retrieving the challenge for accepted user. The following three steps then involve speaker recognition including the user responding to the challenge by text-dependent voice, server authenticating the response, and finally server accepting/rejecting the user. In order to implement fingerprint watermarking, i.e. incorporating the memorable word as a watermark message into the fingerprint image, an algorithm of five steps has been developed. The first three novel steps having to do with the fingerprint image enhancement (CLAHE with 'Clip Limit', standard deviation analysis and sliding neighborhood) have been followed with further two steps for embedding, and extracting the watermark into the enhanced fingerprint image utilising Discrete Wavelet Transform (DWT). In the speaker recognition stage, the limitations of this technique in wireless communication have been addressed by sending voice feature (cepstral coefficients) instead of raw sample. This scheme is to reap the advantages of reducing the transmission time and dependency of the data on communication channel, together with no loss of packet. Finally, the obtained results have verified the claims.EThOS - Electronic Theses Online ServiceGBUnited Kingdo