지문 영상 잡음 제거 및 복원을 위한 심층 합성곱 신경망

학위논문 (석사) -- 서울대학교 대학원 : 자연과학대학 협동과정 계산과학전공, 2021. 2. 강명주.Biometric authentication using fingerprints requires a method for image denoising and inpainting to extract fingerprints from degraded fingerprint images. A few deep learning models for fingerprint image denoising and inpainting were proposed in ChaLearn LAP Inpainting Competition - Track 3, ECCV 2018. In this thesis, a new deep learning model for fingerprint image denoising is proposed. The proposed model is adapted from FusionNet, which is a convolutional neural network based deep learning model for image segmentation. The performance of the proposed model was demonstrated using the dataset from the ECCV 2018 ChaLearn Competition. It was shown that the proposed model obtains better results compared with the models that achieved high performances in the competition.지문을 사용한 생체 인식 인증은 품질이 저하된 지문 영상에서 지문을 추출하기 위한 영상 잡음 제거 및 복원 방법을 필요로 한다. 지문 영상 잡음 제거 및 복원을 위한 몇 가지 딥러닝 모델이 ChaLearn LAP Inpainting Competition - Track 3, ECCV 2018에서 제안되었다. 본 논문에서는 지문 영상 잡음 제거를 위한 새로운 딥러닝 모델을 제안한다. 제안된 모델은 영상 분할을 위한 합성곱 신경망 기반 딥러닝 모델인 FusionNet을 수정하여 작성하였다. 제안된 모델의 성능은 ChaLearn Competition의 데이터셋을 사용하여 검증되었다. 이를 통해 제안된 모델이 대회에서 높은 성능을 획득한 다른 모델들에 비하여 더 나은 결과를 얻음을 확인하였다.Abstract i Contents ii 1 Introduction 1 2 Related Work 3 2.1 Residual Neural Network . . . . . . . . . . . . . . . . . . . . . . . . 3 2.2 Convolutional Neural Networks for Semantic Segmentation . . . . . . 4 2.2.1 U-Net . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.2.2 FusionNet . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.3 Recent Trends in Fingerprint Image Denoising . . . . . . . . . . . . . 6 3 Proposed Model 7 3.1 Model Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 3.2 Architecture Detail . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 3.2.1 Residual Block . . . . . . . . . . . . . . . . . . . . . . . . . 9 3.2.2 Encoder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.2.3 Bridge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.2.4 Decoder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.3 Loss Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 4 Experiments 13 4.1 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 4.2 Evaluation Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 4.3 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 4.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . 16 4.4.1 Ablation Study . . . . . . . . . . . . . . . . . . . . . . . . . 16 4.4.2 Comparison with Other Models . . . . . . . . . . . . . . . . 17 5 Conclusion 21 Abstract (In Korean)Maste

CardioCam: Leveraging Camera on Mobile Devices to Verify Users While Their Heart is Pumping

With the increasing prevalence of mobile and IoT devices (e.g., smartphones, tablets, smart-home appliances), massive private and sensitive information are stored on these devices. To prevent unauthorized access on these devices, existing user verification solutions either rely on the complexity of user-defined secrets (e.g., password) or resort to specialized biometric sensors (e.g., fingerprint reader), but the users may still suffer from various attacks, such as password theft, shoulder surfing, smudge, and forged biometrics attacks. In this paper, we propose, CardioCam, a low-cost, general, hard-to-forge user verification system leveraging the unique cardiac biometrics extracted from the readily available built-in cameras in mobile and IoT devices. We demonstrate that the unique cardiac features can be extracted from the cardiac motion patterns in fingertips, by pressing on the built-in camera. To mitigate the impacts of various ambient lighting conditions and human movements under practical scenarios, CardioCam develops a gradient-based technique to optimize the camera configuration, and dynamically selects the most sensitive pixels in a camera frame to extract reliable cardiac motion patterns. Furthermore, the morphological characteristic analysis is deployed to derive user-specific cardiac features, and a feature transformation scheme grounded on Principle Component Analysis (PCA) is developed to enhance the robustness of cardiac biometrics for effective user verification. With the prototyped system, extensive experiments involving 25 subjects are conducted to demonstrate that CardioCam can achieve effective and reliable user verification with over 99% average true positive rate (TPR) while maintaining the false positive rate (FPR) as low as 4%

DragID: A Gesture Based Authentication System

Department of Electrical EngineeringWith the use of mobile computing devices with touch screens is becoming widespread. Sensitive personal information is often stored in the mobile devices. Smart device users use applications with sensitive personal data such as in online banking. To protect personal information, code based screen unlock methods are used so far. However, these methods are vulnerable to shoulder surfing or smudge attacks. To build a secure unlocking methods we propose DragID, a flexible gesture and biometric based user authentication. Based on the human modeling, DragID authenticates users by using 6 input sources of touch screens. From the input sources, we build 25 fine grained features such as origin of hand, finger radius, velocity, gravity, perpendicular and so on. As modeling the human hand, inour method, features such as radius or origin is difficult to imitate. These features are useful for authentication. In order to authenticate, we use a popular machine learning method, support vector machine. This method prevents attackers reproducing the exact same drag patterns. In the experiments, we implemented DragID on Samsung Galaxy Note2, collected 147379 drag samples from 17 volunteers, and conducted real-world experiments. Our method outperforms Luca???s method and achieves 89.49% and 0.36% of true positive and false positive. In addition, we achieve 92.33% of TPR in case we implement sequence technique.ope

An Approach to Software Development for Continuous Authentication of Smart Wearable Device Users

abstract: With the recent expansion in the use of wearable technology, a large number of users access personal data with these smart devices. The consumer market of wearables includes smartwatches, health and fitness bands, and gesture control armbands. These smart devices enable users to communicate with each other, control other devices, relax and work out more effectively. As part of their functionality, these devices store, transmit, and/or process sensitive user personal data, perhaps biological and location data, making them an abundant source of confidential user information. Thus, prevention of unauthorized access to wearables is necessary. In fact, it is important to effectively authenticate users to prevent intentional misuse or alteration of individual data. Current authentication methods for the legitimate users of smart wearable devices utilize passcodes, and graphical pattern based locks. These methods have the following problems: (1) passcodes can be stolen or copied, (2) they depend on conscious user inputs, which can be undesirable to a user, (3) they authenticate the user only at the beginning of the usage session, and (4) they do not consider user behavior or they do not adapt to evolving user behavior. In this thesis, an approach is presented for developing software for continuous authentication of the legitimate user of a smart wearable device. With this approach, the legitimate user of a smart wearable device can be authenticated based on the user's behavioral biometrics in the form of motion gestures extracted from the embedded sensors of the smart wearable device. The continuous authentication of this approach is accomplished by adapting the authentication to user's gesture pattern changes. This approach is demonstrated by using two comprehensive datasets generated by two research groups, and it is shown that this approach achieves better performance than existing methods.Dissertation/ThesisMasters Thesis Software Engineering 201

Fingerprint classification with combined neural networks

Biometric identification has been widely used in identifying a genuine person from an impostor. Fingerprint identification is becoming a very popular biometric identification technique because it has special properties: fingerprints are unique and unchangeable. With increased processing capability of computers and larger the size of fingerprint databases are increased, the demand for higher speed processing and greater processing capacity for automatic fingerprint identification systems (AFIS) has increased. APIS consists of fingerprint feature acquisition, fingerprint classification and fingerprint matching. Fingerprint classification plays a key role in fingerprint identification as efficient and accurate algorithms cannot only reduce the search time for searching large fingerprint databases, but they can also reduce the number of fingerprints that need to be searched.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Vulnerability assessment in the use of biometrics in unsupervised environments

Mención Internacional en el título 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 últimas décadas, hemos asistido a un despliegue a gran escala de los sistemas biométricos en diferentes aplicaciones de la vida cotidiana, sustituyendo a los métodos de reconocimiento tradicionales, como las contraseñas y los tokens. Actualmente los sistemas biométricos ya forman parte de nuestra vida cotidiana: es habitual emplear estos sistemas para que nos proporcionen acceso a nuestros dispositivos electrónicos (teléfonos inteligentes, tabletas, ordenadores portátiles, etc.) usando nuestras características biométricas. Además, accedemos a nuestras cuentas bancarias, realizamos diversos tipos de pagos y transacciones utilizando los sensores biométricos integrados en nuestros dispositivos. Por otra parte, diferentes organizaciones, empresas e instituciones utilizan soluciones basadas en la biometría para el control de acceso. A escala nacional, las autoridades policiales y de control fronterizo utilizan dispositivos de reconocimiento biométrico con fines de identificación y verificación individual. Por lo tanto, en todas estas aplicaciones se confía en que los sistemas biométricos proporcionen un reconocimiento seguro en el que solo el usuario genuino pueda ser reconocido como tal. Además, el sistema biométrico debe garantizar que un individuo no pueda ser identificado como otra persona. En el estado del arte, hay un número sorprendente de experimentos que muestran la posibilidad de robar las características biométricas de alguien, y utilizarlas para crear un rasgo biométrico artificial que puede ser utilizado por un atacante con el fin de reclamar la identidad del usuario genuino. También se han dado casos reales de personas que lograron engañar al sistema de reconocimiento biométrico en aeropuertos y teléfonos 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 presentación 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 biométrico genérico analizando sus puntos vulnerables con especial atención a los ataques de presentación. El estudio revisa la literatura sobre ataques de presentación y las soluciones correspondientes, es decir, los mecanismos de detección de ataques de presentación, para seis modalidades biométricas: huella dactilar, rostro, iris, vascular, firma manuscrita y voz. Además, se proporciona una nueva taxonomía para los mecanismos de detección de ataques de presentación. La taxonomía propuesta ayuda a comprender el problema de los ataques de presentación y la forma en que la literatura ha tratado de abordarlo. Esta taxonomía presenta un punto de partida para iniciar nuevas investigaciones que propongan novedosos mecanismos de detección de ataques de presentación. En la segunda fase, se desarrolla una metodología de evaluación a partir de dos fuentes: (1) la norma ISO/IEC 30107, y (2) Common Evaluation Methodology por el Common Criteria. La metodología desarrollada considera dos aspectos importantes del mecanismo de detección de ataques de presentación (1) la resistencia del mecanismo a los ataques de presentación, y (2) la correspondiente amenaza del ataque estudiado. Para el primer punto, se han de señalar las capacidades técnicas del mecanismo y cómo influyen en la seguridad y la facilidad de uso del sistema biométrico. Para el segundo aspecto se debe llevar a cabo una evaluación de la vulnerabilidad, teniendo en cuenta todos los factores que afectan al potencial de ataque. Por último, siguiendo esta metodología, se lleva a cabo una recogida de datos que incluye 7128 vídeos de huellas dactilares genuinas y de presentación de ataques. Los datos se recogen utilizando dos tecnologías de sensor, dos escenarios de presentación y considerando siete tipos de instrumentos de ataque. La base de datos se utiliza para desarrollar y evaluar mecanismos dinámicos de detección de ataques de presentación que explotan las características espacio-temporales de las huellas dactilares. En la fase final, se desarrolla un conjunto de mecanismos novedosos de detección de ataques de presentación que explotan las características dinámicas causadas por los fenómenos naturales de las huellas dactilares, como la transpiración y la elasticidad. Los resultados de la evaluación muestran una capacidad eficiente de detección 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 Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Cristina Conde Vila.- Secretario: Mariano López García.- Vocal: Farzin Derav

Recent Application in Biometrics

In the recent years, a number of recognition and authentication systems based on biometric measurements have been proposed. Algorithms and sensors have been developed to acquire and process many different biometric traits. Moreover, the biometric technology is being used in novel ways, with potential commercial and practical implications to our daily activities. The key objective of the book is to provide a collection of comprehensive references on some recent theoretical development as well as novel applications in biometrics. The topics covered in this book reflect well both aspects of development. They include biometric sample quality, privacy preserving and cancellable biometrics, contactless biometrics, novel and unconventional biometrics, and the technical challenges in implementing the technology in portable devices. The book consists of 15 chapters. It is divided into four sections, namely, biometric applications on mobile platforms, cancelable biometrics, biometric encryption, and other applications. The book was reviewed by editors Dr. Jucheng Yang and Dr. Norman Poh. We deeply appreciate the efforts of our guest editors: Dr. Girija Chetty, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park and Dr. Sook Yoon, as well as a number of anonymous reviewers

Lowering levels of heritage crime via novel chemical procedures

The work reported here focused on developing innovative ways of addressing heritage crime, and by doing so, protecting and preserving the historical assets found nationwide. The interdisciplinary focus, linking chemistry and criminology was imperative, and this connection is a novel way in which the issue of heritage crime can be addressed. A survey was completed noting the key issues faced, and helped develop and report an understanding of the general attitudes towards heritage sites across the country. The results obtained here facilitated the chemistry research from this point, channelling the investigations in the appropriate pathway, as well as justifying the work done from that point onwards. A large focus during the course of the research was that of metal theft. With this in mind, there were subsequent attempts to develop a novel and non-invasive technique, which could help lower levels of such crime at heritage sites. Early work concentrated on detecting trace levels of metals commonly found at heritage sites such as copper and lead, and their interaction with the surface of the skin. The metals were shown to form characteristically coloured complexes when reacting with components of the skin itself, thus confirming an individuals recent contact with the relevant metal. This work progressed further via analysis of the metal itself post contact with a human finger. Again, remaining non-invasive was imperative, and a technique focusing on the development of fingerprints from the surface of copper and its alloys, via utilization of gelatine lifters, was studied extensively. Optimizing this technique via a study on the effects of the environment a piece of metal was stored in prior to development via rubeanic acid solution further developed the understanding of this method. Desiccation and the resultant reduction in humidity proved to be effective in enhancing the quality of fingerprint produced. This also had potential impact outside of the heritage crime focus, with fingerprint development from surfaces such as bullet casings being a particularly noteworthy example. Studies relating to why a change in environment enhanced the quality of fingerprint developed were conducted, with several fingermark constituents being shown to react with rubeanate solution. 2 Because of high theft levels highlighted within the survey, efforts were made to produce information regarding stone samples found in a range of different environments. Laser induced breakdown spectroscopy (LIBS) was used as a method of non-invasively analysing loose material from several gravestones removed via the gelatine lifters. As well as producing information unique to each piece of stone analysed, this also highlighted a novel use of the analytical equipment itself