567 research outputs found
Análise de propriedades intrĂnsecas e extrĂnsecas de amostras biomĂ©tricas para detecção de ataques de apresentação
Orientadores: Anderson de Rezende Rocha, HĂ©lio PedriniTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Os recentes avanços nas áreas de pesquisa em biometria, forense e segurança da informação trouxeram importantes melhorias na eficácia dos sistemas de reconhecimento biomĂ©tricos. No entanto, um desafio ainda em aberto Ă© a vulnerabilidade de tais sistemas contra ataques de apresentação, nos quais os usuários impostores criam amostras sintĂ©ticas, a partir das informações biomĂ©tricas originais de um usuário legĂtimo, e as apresentam ao sensor de aquisição procurando se autenticar como um usuário válido. Dependendo da modalidade biomĂ©trica, os tipos de ataque variam de acordo com o tipo de material usado para construir as amostras sintĂ©ticas. Por exemplo, em biometria facial, uma tentativa de ataque Ă© caracterizada quando um usuário impostor apresenta ao sensor de aquisição uma fotografia, um vĂdeo digital ou uma máscara 3D com as informações faciais de um usuário-alvo. Em sistemas de biometria baseados em Ăris, os ataques de apresentação podem ser realizados com fotografias impressas ou com lentes de contato contendo os padrões de Ăris de um usuário-alvo ou mesmo padrões de textura sintĂ©ticas. Nos sistemas biomĂ©tricos de impressĂŁo digital, os usuários impostores podem enganar o sensor biomĂ©trico usando rĂ©plicas dos padrões de impressĂŁo digital construĂdas com materiais sintĂ©ticos, como látex, massa de modelar, silicone, entre outros. Esta pesquisa teve como objetivo o desenvolvimento de soluções para detecção de ataques de apresentação considerando os sistemas biomĂ©tricos faciais, de Ăris e de impressĂŁo digital. As linhas de investigação apresentadas nesta tese incluem o desenvolvimento de representações baseadas nas informações espaciais, temporais e espectrais da assinatura de ruĂdo; em propriedades intrĂnsecas das amostras biomĂ©tricas (e.g., mapas de albedo, de reflectância e de profundidade) e em tĂ©cnicas de aprendizagem supervisionada de caracterĂsticas. Os principais resultados e contribuições apresentadas nesta tese incluem: a criação de um grande conjunto de dados publicamente disponĂvel contendo aproximadamente 17K videos de simulações de ataques de apresentações e de acessos genuĂnos em um sistema biomĂ©trico facial, os quais foram coletados com a autorização do ComitĂŞ de Ética em Pesquisa da Unicamp; o desenvolvimento de novas abordagens para modelagem e análise de propriedades extrĂnsecas das amostras biomĂ©tricas relacionadas aos artefatos que sĂŁo adicionados durante a fabricação das amostras sintĂ©ticas e sua captura pelo sensor de aquisição, cujos resultados de desempenho foram superiores a diversos mĂ©todos propostos na literature que se utilizam de mĂ©todos tradicionais de análise de images (e.g., análise de textura); a investigação de uma abordagem baseada na análise de propriedades intrĂnsecas das faces, estimadas a partir da informação de sombras presentes em sua superfĂcie; e, por fim, a investigação de diferentes abordagens baseadas em redes neurais convolucionais para o aprendizado automático de caracterĂsticas relacionadas ao nosso problema, cujos resultados foram superiores ou competitivos aos mĂ©todos considerados estado da arte para as diferentes modalidades biomĂ©tricas consideradas nesta tese. A pesquisa tambĂ©m considerou o projeto de eficientes redes neurais com arquiteturas rasas capazes de aprender caracterĂsticas relacionadas ao nosso problema a partir de pequenos conjuntos de dados disponĂveis para o desenvolvimento e a avaliação de soluções para a detecção de ataques de apresentaçãoAbstract: Recent advances in biometrics, information forensics, and security have improved the recognition effectiveness of biometric systems. However, an ever-growing challenge is the vulnerability of such systems against presentation attacks, in which impostor users create synthetic samples from the original biometric information of a legitimate user and show them to the acquisition sensor seeking to authenticate themselves as legitimate users. Depending on the trait used by the biometric authentication, the attack types vary with the type of material used to build the synthetic samples. For instance, in facial biometric systems, an attempted attack is characterized by the type of material the impostor uses such as a photograph, a digital video, or a 3D mask with the facial information of a target user. In iris-based biometrics, presentation attacks can be accomplished with printout photographs or with contact lenses containing the iris patterns of a target user or even synthetic texture patterns. In fingerprint biometric systems, impostor users can deceive the authentication process using replicas of the fingerprint patterns built with synthetic materials such as latex, play-doh, silicone, among others. This research aimed at developing presentation attack detection (PAD) solutions whose objective is to detect attempted attacks considering different attack types, in each modality. The lines of investigation presented in this thesis aimed at devising and developing representations based on spatial, temporal and spectral information from noise signature, intrinsic properties of the biometric data (e.g., albedo, reflectance, and depth maps), and supervised feature learning techniques, taking into account different testing scenarios including cross-sensor, intra-, and inter-dataset scenarios. The main findings and contributions presented in this thesis include: the creation of a large and publicly available benchmark containing 17K videos of presentation attacks and bona-fide presentations simulations in a facial biometric system, whose collect were formally authorized by the Research Ethics Committee at Unicamp; the development of novel approaches to modeling and analysis of extrinsic properties of biometric samples related to artifacts added during the manufacturing of the synthetic samples and their capture by the acquisition sensor, whose results were superior to several approaches published in the literature that use traditional methods for image analysis (e.g., texture-based analysis); the investigation of an approach based on the analysis of intrinsic properties of faces, estimated from the information of shadows present on their surface; and the investigation of different approaches to automatically learning representations related to our problem, whose results were superior or competitive to state-of-the-art methods for the biometric modalities considered in this thesis. We also considered in this research the design of efficient neural networks with shallow architectures capable of learning characteristics related to our problem from small sets of data available to develop and evaluate PAD solutionsDoutoradoCiĂŞncia da ComputaçãoDoutor em CiĂŞncia da Computação140069/2016-0
CNPq, 142110/2017-5CAPESCNP
Vulnerability assessment in the use of biometrics in unsupervised environments
MenciĂłn Internacional en el tĂtulo de doctorIn the last few decades, we have witnessed a large-scale deployment of biometric systems in different life applications replacing the traditional recognition methods such as passwords and tokens. We approached a time where we use biometric systems in our daily life. On a personal scale, the authentication to our electronic devices (smartphones, tablets, laptops, etc.) utilizes biometric characteristics to provide access permission. Moreover, we access our bank accounts, perform various types of payments and transactions using the biometric sensors integrated into our devices. On the other hand, different organizations, companies, and institutions use biometric-based solutions for access control. On the national scale, police authorities and border control measures use biometric recognition devices for individual identification and verification purposes.
Therefore, biometric systems are relied upon to provide a secured recognition where only the genuine user can be recognized as being himself. Moreover, the biometric system should ensure that an individual cannot be identified as someone else. In the literature, there are a surprising number of experiments that show the possibility of stealing someone’s biometric characteristics and use it to create an artificial biometric trait that can be used by an attacker to claim the identity of the genuine user. There were also real cases of people who successfully fooled the biometric recognition system in airports and smartphones [1]–[3]. That urges the necessity to investigate the potential threats and propose countermeasures that ensure high levels of security and user convenience.
Consequently, performing security evaluations is vital to identify: (1) the security flaws in biometric systems, (2) the possible threats that may target the defined flaws, and (3) measurements that describe the technical competence of the biometric system security. Identifying the system vulnerabilities leads to proposing adequate security solutions that assist in achieving higher integrity.
This thesis aims to investigate the vulnerability of fingerprint modality to presentation attacks in unsupervised environments, then implement mechanisms to detect those attacks and avoid the misuse of the system. To achieve these objectives, the thesis is carried out in the following three phases.
In the first phase, the generic biometric system scheme is studied by analyzing the vulnerable points with special attention to the vulnerability to presentation attacks. The study reviews the literature in presentation attack and the corresponding solutions, i.e. presentation attack detection mechanisms, for six biometric modalities: fingerprint, face, iris, vascular, handwritten signature, and voice. Moreover, it provides a new taxonomy for presentation attack detection mechanisms. The proposed taxonomy helps to comprehend the issue of presentation attacks and how the literature tried to address it. The taxonomy represents a starting point to initialize new investigations that propose novel presentation attack detection mechanisms.
In the second phase, an evaluation methodology is developed from two sources: (1) the ISO/IEC 30107 standard, and (2) the Common Evaluation Methodology by the Common Criteria. The developed methodology characterizes two main aspects of the presentation attack detection mechanism: (1) the resistance of the mechanism to presentation attacks, and (2) the corresponding threat of the studied attack. The first part is conducted by showing the mechanism's technical capabilities and how it influences the security and ease-of-use of the biometric system. The second part is done by performing a vulnerability assessment considering all the factors that affect the attack potential. Finally, a data collection is carried out, including 7128 fingerprint videos of bona fide and attack presentation. The data is collected using two sensing technologies, two presentation scenarios, and considering seven attack species. The database is used to develop dynamic presentation attack detection mechanisms that exploit the fingerprint spatio-temporal features.
In the final phase, a set of novel presentation attack detection mechanisms is developed exploiting the dynamic features caused by the natural fingerprint phenomena such as perspiration and elasticity. The evaluation results show an efficient capability to detect attacks where, in some configurations, the mechanisms are capable of eliminating some attack species and mitigating the rest of the species while keeping the user convenience at a high level.En las Ăşltimas dĂ©cadas, hemos asistido a un despliegue a gran escala de los sistemas biomĂ©tricos en diferentes aplicaciones de la vida cotidiana, sustituyendo a los mĂ©todos de reconocimiento tradicionales, como las contraseñas y los tokens. Actualmente los sistemas biomĂ©tricos ya forman parte de nuestra vida cotidiana: es habitual emplear estos sistemas para que nos proporcionen acceso a nuestros dispositivos electrĂłnicos (telĂ©fonos inteligentes, tabletas, ordenadores portátiles, etc.) usando nuestras caracterĂsticas biomĂ©tricas. Además, accedemos a nuestras cuentas bancarias, realizamos diversos tipos de pagos y transacciones utilizando los sensores biomĂ©tricos integrados en nuestros dispositivos. Por otra parte, diferentes organizaciones, empresas e instituciones utilizan soluciones basadas en la biometrĂa para el control de acceso. A escala nacional, las autoridades policiales y de control fronterizo utilizan dispositivos de reconocimiento biomĂ©trico con fines de identificaciĂłn y verificaciĂłn individual.
Por lo tanto, en todas estas aplicaciones se confĂa en que los sistemas biomĂ©tricos proporcionen un reconocimiento seguro en el que solo el usuario genuino pueda ser reconocido como tal. Además, el sistema biomĂ©trico debe garantizar que un individuo no pueda ser identificado como otra persona. En el estado del arte, hay un nĂşmero sorprendente de experimentos que muestran la posibilidad de robar las caracterĂsticas biomĂ©tricas de alguien, y utilizarlas para crear un rasgo biomĂ©trico artificial que puede ser utilizado por un atacante con el fin de reclamar la identidad del usuario genuino. TambiĂ©n se han dado casos reales de personas que lograron engañar al sistema de reconocimiento biomĂ©trico en aeropuertos y telĂ©fonos inteligentes [1]–[3]. Esto hace que sea necesario investigar estas posibles amenazas y proponer contramedidas que garanticen altos niveles de seguridad y comodidad para el usuario.
En consecuencia, es vital la realización de evaluaciones de seguridad para identificar (1) los fallos de seguridad de los sistemas biométricos, (2) las posibles amenazas que pueden explotar estos fallos, y (3) las medidas que aumentan la seguridad del sistema biométrico reduciendo estas amenazas. La identificación de las vulnerabilidades del sistema lleva a proponer soluciones de seguridad adecuadas que ayuden a conseguir una mayor integridad.
Esta tesis tiene como objetivo investigar la vulnerabilidad en los sistemas de modalidad de huella dactilar a los ataques de presentaciĂłn en entornos no supervisados, para luego implementar mecanismos que permitan detectar dichos ataques y evitar el mal uso del sistema. Para lograr estos objetivos, la tesis se desarrolla en las siguientes tres fases.
En la primera fase, se estudia el esquema del sistema biomĂ©trico genĂ©rico analizando sus puntos vulnerables con especial atenciĂłn a los ataques de presentaciĂłn. El estudio revisa la literatura sobre ataques de presentaciĂłn y las soluciones correspondientes, es decir, los mecanismos de detecciĂłn de ataques de presentaciĂłn, para seis modalidades biomĂ©tricas: huella dactilar, rostro, iris, vascular, firma manuscrita y voz. Además, se proporciona una nueva taxonomĂa para los mecanismos de detecciĂłn de ataques de presentaciĂłn. La taxonomĂa propuesta ayuda a comprender el problema de los ataques de presentaciĂłn y la forma en que la literatura ha tratado de abordarlo. Esta taxonomĂa presenta un punto de partida para iniciar nuevas investigaciones que propongan novedosos mecanismos de detecciĂłn de ataques de presentaciĂłn.
En la segunda fase, se desarrolla una metodologĂa de evaluaciĂłn a partir de dos fuentes: (1) la norma ISO/IEC 30107, y (2) Common Evaluation Methodology por el Common Criteria. La metodologĂa desarrollada considera dos aspectos importantes del mecanismo de detecciĂłn de ataques de presentaciĂłn (1) la resistencia del mecanismo a los ataques de presentaciĂłn, y (2) la correspondiente amenaza del ataque estudiado. Para el primer punto, se han de señalar las capacidades tĂ©cnicas del mecanismo y cĂłmo influyen en la seguridad y la facilidad de uso del sistema biomĂ©trico. Para el segundo aspecto se debe llevar a cabo una evaluaciĂłn de la vulnerabilidad, teniendo en cuenta todos los factores que afectan al potencial de ataque. Por Ăşltimo, siguiendo esta metodologĂa, se lleva a cabo una recogida de datos que incluye 7128 vĂdeos de huellas dactilares genuinas y de presentaciĂłn de ataques. Los datos se recogen utilizando dos tecnologĂas de sensor, dos escenarios de presentaciĂłn y considerando siete tipos de instrumentos de ataque. La base de datos se utiliza para desarrollar y evaluar mecanismos dinámicos de detecciĂłn de ataques de presentaciĂłn que explotan las caracterĂsticas espacio-temporales de las huellas dactilares.
En la fase final, se desarrolla un conjunto de mecanismos novedosos de detecciĂłn de ataques de presentaciĂłn que explotan las caracterĂsticas dinámicas causadas por los fenĂłmenos naturales de las huellas dactilares, como la transpiraciĂłn y la elasticidad. Los resultados de la evaluaciĂłn muestran una capacidad eficiente de detecciĂłn de ataques en la que, en algunas configuraciones, los mecanismos son capaces de eliminar completamente algunos tipos de instrumentos de ataque y mitigar el resto de los tipos manteniendo la comodidad del usuario en un nivel alto.Programa de Doctorado en IngenierĂa ElĂ©ctrica, ElectrĂłnica y Automática por la Universidad Carlos III de MadridPresidente: Cristina Conde Vila.- Secretario: Mariano LĂłpez GarcĂa.- Vocal: Farzin Derav
Biometric Liveness Detection Using Gaze Information
This thesis is concerned with liveness detection for biometric systems and in particular for face recognition systems. Biometric systems are well studied and have the potential to provide satisfactory solutions for a variety of applications.
However, presentation attacks (spoofng), where an attempt is made at subverting them system by making a deliberate presentation at the sensor is a serious challenge to their use in unattended applications. Liveness detection techniques can help with protecting biometric systems from attacks made through the presentation of artefacts and recordings at the sensor. In this work novel techniques for liveness detection are presented using gaze information.
The notion of natural gaze stability is introduced and used to develop a number of novel features that rely on directing the gaze of the user and establishing its behaviour. These features are then used to develop systems for detecting spoofng attempts. The attack scenarios considered in this work include the use of hand held photos and photo masks as well as video reply to subvert the system. The proposed features and systems based on them were evaluated extensively using data captured from genuine and fake attempts.
The results of the evaluations indicate that gaze-based features can be used to discriminate between genuine and imposter. Combining features through feature selection and score fusion substantially improved the performance of the proposed features
Deep Learning for Face Anti-Spoofing: A Survey
Face anti-spoofing (FAS) has lately attracted increasing attention due to its
vital role in securing face recognition systems from presentation attacks
(PAs). As more and more realistic PAs with novel types spring up, traditional
FAS methods based on handcrafted features become unreliable due to their
limited representation capacity. With the emergence of large-scale academic
datasets in the recent decade, deep learning based FAS achieves remarkable
performance and dominates this area. However, existing reviews in this field
mainly focus on the handcrafted features, which are outdated and uninspiring
for the progress of FAS community. In this paper, to stimulate future research,
we present the first comprehensive review of recent advances in deep learning
based FAS. It covers several novel and insightful components: 1) besides
supervision with binary label (e.g., '0' for bonafide vs. '1' for PAs), we also
investigate recent methods with pixel-wise supervision (e.g., pseudo depth
map); 2) in addition to traditional intra-dataset evaluation, we collect and
analyze the latest methods specially designed for domain generalization and
open-set FAS; and 3) besides commercial RGB camera, we summarize the deep
learning applications under multi-modal (e.g., depth and infrared) or
specialized (e.g., light field and flash) sensors. We conclude this survey by
emphasizing current open issues and highlighting potential prospects.Comment: IEEE Transactions on Pattern Analysis and Machine Intelligence
(TPAMI
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