Mind the Gap:A practical framework for classifiers in a forensic context

In this paper, we present a practical framework that addresses six, mostly forensic, aspects that can be considered during the design and evaluation of biometric classifiers for the purpose of forensic evidence evaluation. Forensic evidence evaluation is a central activity in forensic case work, it includes the assessment of strength of evidence of trace and reference specimens and its outcome may be used in a court of law. The addressed aspects consider the modality and features, the biometric score and its forensic use, and choice and evaluation of several performance characteristics and metrics. The aim of the framework is to make the design and evaluation choices more transparent. We also present two applications of the framework pertaining to forensic face recognition. Using the framework, we can demonstrate large and explainable variations in discriminating power between subjects

Reconocimiento de huellas dactilares para aplicaciones forenses

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Tecnología Electrónica y de las Comunicaciones. Fecha de lectura: mayo de 2015The author was awarded with a European Commission Marie Curie Fellowship under the Innovative Training Networks (ITN) in the project Bayesian Biometrics for Forensics (BBfor2, FP7-PEOPLE-ITN-2008) under Grant Agreement number 238803 between 2011 and 2013. The author was also funded through the European Union Project - Biometrics Evaluation and Testing (BEAT) for 2014 and 2015 which supported the research summarized in this Dissertatio

Speaker Recognition in Unconstrained Environments

Speaker recognition is applied in smart home devices, interactive voice response systems, call centers, online banking and payment solutions as well as in forensic scenarios. This dissertation is concerned with speaker recognition systems in unconstrained environments. Before this dissertation, research on making better decisions in unconstrained environments was insufficient. Aside from decision making, unconstrained environments imply two other subjects: security and privacy. Within the scope of this dissertation, these research subjects are regarded as both security against short-term replay attacks and privacy preservation within state-of-the-art biometric voice comparators in the light of a potential leak of biometric data. The aforementioned research subjects are united in this dissertation to sustain good decision making processes facing uncertainty from varying signal quality and to strengthen security as well as preserve privacy. Conventionally, biometric comparators are trained to classify between mated and non-mated reference,--,probe pairs under idealistic conditions but are expected to operate well in the real world. However, the more the voice signal quality degrades, the more erroneous decisions are made. The severity of their impact depends on the requirements of a biometric application. In this dissertation, quality estimates are proposed and employed for the purpose of making better decisions on average in a formalized way (quantitative method), while the specifications of decision requirements of a biometric application remain unknown. By using the Bayesian decision framework, the specification of application-depending decision requirements is formalized, outlining operating points: the decision thresholds. The assessed quality conditions combine ambient and biometric noise, both of which occurring in commercial as well as in forensic application scenarios. Dual-use (civil and governmental) technology is investigated. As it seems unfeasible to train systems for every possible signal degradation, a low amount of quality conditions is used. After examining the impact of degrading signal quality on biometric feature extraction, the extraction is assumed ideal in order to conduct a fair benchmark. This dissertation proposes and investigates methods for propagating information about quality to decision making. By employing quality estimates, a biometric system's output (comparison scores) is normalized in order to ensure that each score encodes the least-favorable decision trade-off in its value. Application development is segregated from requirement specification. Furthermore, class discrimination and score calibration performance is improved over all decision requirements for real world applications. In contrast to the ISOIEC 19795-1:2006 standard on biometric performance (error rates), this dissertation is based on biometric inference for probabilistic decision making (subject to prior probabilities and cost terms). This dissertation elaborates on the paradigm shift from requirements by error rates to requirements by beliefs in priors and costs. Binary decision error trade-off plots are proposed, interrelating error rates with prior and cost beliefs, i.e., formalized decision requirements. Verbal tags are introduced to summarize categories of least-favorable decisions: the plot's canvas follows from Bayesian decision theory. Empirical error rates are plotted, encoding categories of decision trade-offs by line styles. Performance is visualized in the latent decision subspace for evaluating empirical performance regarding changes in prior and cost based decision requirements. Security against short-term audio replay attacks (a collage of sound units such as phonemes and syllables) is strengthened. The unit-selection attack is posed by the ASVspoof 2015 challenge (English speech data), representing the most difficult to detect voice presentation attack of this challenge. In this dissertation, unit-selection attacks are created for German speech data, where support vector machine and Gaussian mixture model classifiers are trained to detect collage edges in speech representations based on wavelet and Fourier analyses. Competitive results are reached compared to the challenged submissions. Homomorphic encryption is proposed to preserve the privacy of biometric information in the case of database leakage. In this dissertation, log-likelihood ratio scores, representing biometric evidence objectively, are computed in the latent biometric subspace. Conventional comparators rely on the feature extraction to ideally represent biometric information, latent subspace comparators are trained to find ideal representations of the biometric information in voice reference and probe samples to be compared. Two protocols are proposed for the the two-covariance comparison model, a special case of probabilistic linear discriminant analysis. Log-likelihood ratio scores are computed in the encrypted domain based on encrypted representations of the biometric reference and probe. As a consequence, the biometric information conveyed in voice samples is, in contrast to many existing protection schemes, stored protected and without information loss. The first protocol preserves privacy of end-users, requiring one public/private key pair per biometric application. The latter protocol preserves privacy of end-users and comparator vendors with two key pairs. Comparators estimate the biometric evidence in the latent subspace, such that the subspace model requires data protection as well. In both protocols, log-likelihood ratio based decision making meets the requirements of the ISOIEC 24745:2011 biometric information protection standard in terms of unlinkability, irreversibility, and renewability properties of the protected voice data

Anonymizing Speech: Evaluating and Designing Speaker Anonymization Techniques

The growing use of voice user interfaces has led to a surge in the collection and storage of speech data. While data collection allows for the development of efficient tools powering most speech services, it also poses serious privacy issues for users as centralized storage makes private personal speech data vulnerable to cyber threats. With the increasing use of voice-based digital assistants like Amazon's Alexa, Google's Home, and Apple's Siri, and with the increasing ease with which personal speech data can be collected, the risk of malicious use of voice-cloning and speaker/gender/pathological/etc. recognition has increased. This thesis proposes solutions for anonymizing speech and evaluating the degree of the anonymization. In this work, anonymization refers to making personal speech data unlinkable to an identity while maintaining the usefulness (utility) of the speech signal (e.g., access to linguistic content). We start by identifying several challenges that evaluation protocols need to consider to evaluate the degree of privacy protection properly. We clarify how anonymization systems must be configured for evaluation purposes and highlight that many practical deployment configurations do not permit privacy evaluation. Furthermore, we study and examine the most common voice conversion-based anonymization system and identify its weak points before suggesting new methods to overcome some limitations. We isolate all components of the anonymization system to evaluate the degree of speaker PPI associated with each of them. Then, we propose several transformation methods for each component to reduce as much as possible speaker PPI while maintaining utility. We promote anonymization algorithms based on quantization-based transformation as an alternative to the most-used and well-known noise-based approach. Finally, we endeavor a new attack method to invert anonymization.Comment: PhD Thesis Pierre Champion | Universit\'e de Lorraine - INRIA Nancy | for associated source code, see https://github.com/deep-privacy/SA-toolki

Reconocimiento automático de locutor e idioma mediante caracterización acústica de unidades lingüísticas

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Tecnología Electrónica y de las Comunicaciones . Fecha de lectura: 30-06-201

Обеспечение безопасности ядерных материалов на гипотетическом объекте

Предметом исследования являются инсайдерские угрозы, угрозы со стороны, инсайдер в сговоре с угрозами со стороны, уязвимость атомных электростанций, анализ угроз, беспилотные летательные аппараты (беспилотные летательные аппараты), АЭС ВВЭР, категоризация и анализ наиболее уязвимых районов этого объекта , учет и контроль ядерных материалов, проектирование и функции ППС.The subject of the study are insider threats, outsider threats, insider in collusion with outsider threats, nuclear power plant vulnerabilities, analysis of threats that unmanned aerial vehicles (drones) impose on nuclear power plant, design of hypothetical nuclear facility for VVER NPP, categorization

Обеспечение безопасности ядерных материалов на гипотетическом объекте

Предметом исследования являются инсайдерские угрозы, угрозы со стороны, инсайдер в сговоре с угрозами со стороны, уязвимость атомных электростанций, анализ угроз, беспилотные летательные аппараты (беспилотные летательные аппараты), АЭС ВВЭР, категоризация и анализ наиболее уязвимых районов этого объекта , учет и контроль ядерных материалов, проектирование и функции ППС.The subject of the study are insider threats, outsider threats, insider in collusion with outsider threats, nuclear power plant vulnerabilities, analysis of threats that unmanned aerial vehicles (drones) impose on nuclear power plant, design of hypothetical nuclear facility for VVER NPP, categorization

An investigation into 3D printing of osteological remains: the metrology and ethics of virtual anthropology

Three-dimensional (3D) printed human remains are being utilised in courtroom demonstrations of evidence within the UK criminal justice system. This presents a potential issue given that the use of 3D replicas has not yet been empirically tested or validated for use in crime reconstructions. Further, recent movements to critically evaluate the ethics surrounding the presentation of human remains have failed to address the use of 3D printed replica bones. As such, this research addresses the knowledge gap surrounding the accuracy of 3D printed replicas of skeletal elements and investigates how the public feels about the use of 3D printed replicas. Three experimental studies focussed on metrology and identified 3D printed replicas to be accurate to within ± 2.0 mm using computed tomography (CT) scanning, and to within ± 0.2 mm or to 0-5% difference using micro-CT. The potential loss of micromorphological details was also examined and identified that quality control steps were key in identifying and mitigating loss of detail. A fourth experimental study collected data on the opinion of the public of the use of 3D printed human remains in courtroom demonstrations. Respondents were broadly positive and considered that prints can be produced ethically by maintaining the dignity and respect of the decedent. A framework that helps to assess ethical practices was developed as well as an adaptable pathway that can assist with assessing the quality and accuracy of 3D prints. The findings from this research contribute to an empirical evidence base that can underpin future 3D printed crime reconstructions and provides guidance for creating accurate 3D prints that can inform future practice and research endeavours