We introduce Pixie, a novel, camera based two factor authentication solution for mobile and wearable devices. A quick and familiar user action of snapping a photo is sufficient for Pixie to simultaneously perform a graphical password authentication and a physical token based authentication, yet it does not require any expensive, uncommon hardware. Pixie establishes trust based on both the knowledge and possession of an arbitrary physical object readily accessible to the user, called trinket. Users choose their trinkets similar to setting a password, and authenticate by presenting the same trinket to the camera. The fact that the object is the trinket, is secret to the user. Pixie extracts robust, novel features from trinket images, and leverages a supervised learning classifier to effectively address inconsistencies between images of the same trinket captured in different circumstances. Pixie achieved a false accept rate below 0.09 in a brute force attack with 14.3 million authentication attempts, generated with 40,000 trinket images that we captured and collected from public datasets. We identify master images, that match multiple trinkets, and study techniques to reduce their impact. In a user study with 42 participants over 8 days in 3 sessions we found that Pixie outperforms text based passwords on memorability, speed, and user preference. Furthermore, Pixie was easily discoverable by new users and accurate under field use. Users were able to remember their trinkets 2 and 7 days after registering them, without any practice between the 3 test dates.
In contrast to biometrics, Pixie enables users to change the authenticating physical factor, as they change accessories they wear or carry. This reduces the risks from an adversary who has acquired the authentication secret from having lifelong consequences for the victims, thereby mitigating the need for biometric traceability and revocation @cite_16 .
@cite_16
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Abstract Biometrics authentication offers many advantages over conventional authentication systems that rely on possessions or special knowledge. With conventional technology, often the mere possession of an employee ID card is proof of ID, while a password potentially can be used by large groups of colleagues for long times without change. The fact that biometrics authentication is non-repudiable (hard to refute) and, yet, convenient, is among its most important advantages. Biometrics systems, however, suffer from some inherent biometrics-specific security threats. These threats are mainly related to the use of digital signals and the need for additional input devices, though we also discuss brute-force attacks of biometrics systems. There are also problems common to any pattern recognition system. These include “wolves” and “lambs”, and a new group we call “chameleons”. An additional issue with the use of biometrics is the invasion of privacy because the user has to enroll with an image of a body part. We discuss these issues and suggest some methods for mitigating their impact. Biometric data offer a potential source of high-entropy, secret information that can be used in cryptographic protocols provided two issues are addressed: (1) biometric data are not uniformly distributed; and (2) they are not exactly reproducible. Recent work, most notably that of Dodis, Reyzin, and Smith, has shown how these obstacles may be overcome by allowing some auxiliary public information to be reliably sent from a server to the human user. Subsequent work of Boyen has shown how to extend these techniques, in the random oracle model, to enable unidirectional authentication from the user to the server without the assumption of a reliable communication channel. We show two efficient techniques enabling the use of biometric data to achieve mutual authentication or authenticated key exchange over a completely insecure (i.e., adversarially controlled) channel. In addition to achieving stronger security guarantees than the work of Boyen, we improve upon his solution in a number of other respects: we tolerate a broader class of errors and, in one case, improve upon the parameters of his solution and give a proof of security in the standard model. We examine three biometric authentication modalities -- voice, face and gesture -- as well as password entry, on a mobile device, to explore the relative demands on user time, effort, error and task disruption. Our laboratory study provided observations of user actions, strategies, and reactions to the authentication methods. Face and voice biometrics conditions were faster than password entry. Speaking a PIN was the fastest for biometric sample entry, but short-term memory recall was better in the face verification condition. None of the authentication conditions were considered very usable. In conditions that combined two biometric entry methods, the time to acquire the biometric samples was shorter than if acquired separately but they were very unpopular and had high memory task error rates. These quantitative results demonstrate cognitive and motor differences between biometric authentication modalities, and inform policy decisions in selecting authentication methods. The advent of commercial smartwatches provides an intriguing new platform for mobile biometrics. Like their smartphone counterparts, these mobile devices can perform gait-based biometric identification because they too contain an accelerometer and a gyroscope. However, smartwatches have several advantages over smartphones for biometric identification because users almost always wear their watch in the same location and orientation. This location (i.e. the wrist) tends to provide more information about a user's movements than the most common location for smartphones (pockets or handbags). In this paper we show the feasibility of using smartwatches for gait-based biometrics by demonstrating the high levels of accuracy that can result from smartwatch-based identification and authentication models. Applications of smartwatch-based biometrics range from a new authentication challenge for use in a multifactor authentication system to automatic personalization by identifying the user of a shared device. Biometric recognition offers a reliable solution to the problem of user authentication in identity management systems. With the widespread deployment of biometric systems in various applications, there are increasing concerns about the security and privacy of biometric technology. Public acceptance of biometrics technology will depend on the ability of system designers to demonstrate that these systems are robust, have low error rates, and are tamper proof. We present a high-level categorization of the various vulnerabilities of a biometric system and discuss countermeasures that have been proposed to address these vulnerabilities. In particular, we focus on biometric template security which is an important issue because, unlike passwords and tokens, compromised biometric templates cannot be revoked and reissued. Protecting the template is a challenging task due to intrauser variability in the acquired biometric traits. We present an overview of various biometric template protection schemes and discuss their advantages and limitations in terms of security, revocability, and impact on matching accuracy. A template protection scheme with provable security and acceptable recognition performance has thus far remained elusive. Development of such a scheme is crucial as biometric systems are beginning to proliferate into the core physical and information infrastructure of our society. Establishing identity is becoming critical in our vastly interconnected society. Questions such as "Is she really who she claims to be?," "Is this person authorized to use this facility?," or "Is he in the watchlist posted by the government?" are routinely being posed in a variety of scenarios ranging from issuing a driver's license to gaining entry into a country. The need for reliable user authentication techniques has increased in the wake of heightened concerns about security and rapid advancements in networking, communication, and mobility. Biometrics, described as the science of recognizing an individual based on his or her physical or behavioral traits, is beginning to gain acceptance as a legitimate method for determining an individual's identity. Biometric systems have now been deployed in various commercial, civilian, and forensic applications as a means of establishing identity. In this paper, we provide an overview of biometrics and discuss some of the salient research issues that need to be addressed for making biometric technology an effective tool for providing information security. The primary contribution of this overview includes: 1) examining applications where biometric scan solve issues pertaining to information security; 2) enumerating the fundamental challenges encountered by biometric systems in real-world applications; and 3) discussing solutions to address the problems of scalability and security in large-scale authentication systems. Because biometrics-based authentication offers several advantages over other authentication methods, there has been a significant surge in the use of biometrics for user authentication in recent years. It is important that such biometrics-based authentication systems be designed to withstand attacks when employed in security-critical applications, especially in unattended remote applications such as e-commerce. In this paper we outline the inherent strengths of biometrics-based authentication, identify the weak links in systems employing biometrics-based authentication, and present new solutions for eliminating some of these weak links. Although, for illustration purposes, fingerprint authentication is used throughout, our analysis extends to other biometrics-based methods. We present a theoretical framework for the analysis of privacy and security trade-offs in secure biometric authentication systems. We use this framework to conduct a comparative information-theoretic analysis of two biometric systems that are based on linear error correction codes, namely fuzzy commitment and secure sketches. We derive upper bounds for the probability of false rejection (PFR) and false acceptance (PFA) for these systems. We use mutual information to quantify the information leaked about a user's biometric identity, in the scenario where one or multiple biometric enrollments of the user are fully or partially compromised. We also quantify the probability of successful attack (PSA) based on the compromised information. Our analysis reveals that fuzzy commitment and secure sketch systems have identical PFR, PFA, PSA, and information leakage, but secure sketch systems have lower storage requirements. We analyze both single-factor (keyless) and two-factor (key-based) variants of secure biometrics, and consider the most general scenarios in which a single user may provide noisy biometric enrollments at several access control devices, some of which may be subsequently compromised by an attacker. Our analysis highlights the revocability and reusability properties of key-based systems and exposes a subtle design trade-off between reducing information leakage from compromised systems and preventing successful attacks on systems whose data have not been compromised. User authentication is an important step to protect information and in this field face biometrics is advantageous. Face biometrics is natural, easy to use and less human-invasive. Unfortunately, recent work has revealed that face biometrics is vulnerable to spoofing attacks using low-tech cheap equipments. This article presents a countermeasure against such attacks based on the LBP−TOP operator combining both space and time information into a single multiresolution texture descriptor. Experiments carried out with the REPLAY ATTACK database show a Half Total Error Rate (HTER) improvement from 15.16 to 7.60 . We present the results of an MTurk survey (n=383) on the reasons for using and not using biometric authentication systems on smartphones. We focused on Apple's Touch ID as well as Android's Face Unlock as they are the most prevalent systems on the market. For both systems, we categorized the participants as a) current users, b) former users that deactivated it at some point and c) nonusers. The results show that usability is one of the main factors that influences the decision on whether or not to use biometric verification on the smartphone. To our surprise and as opposed to previous research on biometric authentication, privacy and trust issues were not among the most important decision factors. Common authentication methods based on passwords, tokens, or fingerprints perform one-time authentication and rely on users to log out from the computer terminal when they leave. Users often do not log out, however, which is a security risk. The most common solution, inactivity timeouts, inevitably fail security (too long a timeout) or usability (too short a timeout) goals. One solution is to authenticate users continuously while they are using the terminal and automatically log them out when they leave. Several solutions are based on user proximity, but these are not sufficient: they only confirm whether the user is nearby but not whether the user is actually using the terminal. Proposed solutions based on behavioral biometric authentication (e.g., keystroke dynamics) may not be reliable, as a recent study suggests. To address this problem we propose Zero-Effort Bilateral Recurring Authentication (ZEBRA). In ZEBRA, a user wears a bracelet (with a built-in accelerometer, gyroscope, and radio) on her dominant wrist. When the user interacts with a computer terminal, the bracelet records the wrist movement, processes it, and sends it to the terminal. The terminal compares the wrist movement with the inputs it receives from the user (via keyboard and mouse), and confirms the continued presence of the user only if they correlate. Because the bracelet is on the same hand that provides inputs to the terminal, the accelerometer and gyroscope data and input events received by the terminal should correlate because their source is the same - the user's hand movement. In our experiments ZEBRA performed continuous authentication with 85 accuracy in verifying the correct user and identified all adversaries within 11s. For a different threshold that trades security for usability, ZEBRA correctly verified 90 of users and identified all adversaries within 50s. Advances in the field of Information Technology also make Information Security an inseparable part of it. In order to deal with security, Authentication plays an important role. This paper presents a review on the biometric authentication techniques and some future possibilities in this field. In biometrics, a human being needs to be identified based on some characteristic physiological parameters. A wide variety of systems require reliable personal recognition schemes to either confirm or determine the identity of an individual requesting their services. The purpose of such schemes is to ensure that the rendered services are accessed only by a legitimate user, and not anyone else. By using biometrics it is possible to confirm or establish an individual’s identity. The position of biometrics in the current field of Security has been depicted in this work. We have also outlined opinions about the usability of biometric authentication systems, comparison between different techniques and their advantages and disadvantages in this paper. Biometric recognition is an integral component of modern identity management and access control systems. Due to the strong and permanent link between individuals and their biometric traits, exposure of enrolled users? biometric information to adversaries can seriously compromise biometric system security and user privacy. Numerous techniques have been proposed for biometric template protection over the last 20 years. While these techniques are theoretically sound, they seldom guarantee the desired noninvertibility, revocability, and nonlinkability properties without significantly degrading the recognition performance. The objective of this work is to analyze the factors contributing to this performance divide and highlight promising research directions to bridge this gap. The design of invariant biometric representations remains a fundamental problem, despite recent attempts to address this issue through feature adaptation schemes. The difficulty in estimating the statistical distribution of biometric features not only hinders the development of better template protection algorithms but also diminishes the ability to quantify the noninvertibility and nonlinkability of existing algorithms. Finally, achieving nonlinkability without the use of external secrets (e.g., passwords) continues to be a challenging proposition. Further research on the above issues is required to cross the chasm between theory and practice in biometric ?template protection. In this paper, we propose an innovative low-cost biometric authentication scheme based on uniformed Gaussian mixture model (GMM) for secure communications within wireless body area sensor networks (WBASNs), to obviate the cost-prohibitive key exchange problem. In this approach, the sender Inter Pulse Interval (IPI) information is selected as the biometric key for authentication, and a statistical based signing scheme is developed using the uniformed GMM. Experimental analysis shows that the proposed approach effectively protects the communication channels in WBASN with a low computational complexity, significantly removing the need for secret key exchange and critical time synchronization overheads. While biometrics have long been promoted as the future of authentication, the recent introduction of Android face unlock and iPhone fingerprint unlock are among the first large-scale deployments of biometrics for consumers. In a 10participant, within-subjects lab study and a 198-participant online survey, we investigated the usability of these schemes, along with users’ experiences, attitudes, and adoption decisions. Participants in our lab study found both face unlock and fingerprint unlock easy to use in typical scenarios. The notable exception was that face unlock was completely unusable in a dark room. Most participants preferred fingerprint unlock over face unlock or a PIN. In our survey, most fingerprint unlock users perceived it as more secure and convenient than a PIN. In contrast, face unlock users had mixed experiences, and many had stopped using it. We conclude with design recommendations for biometric authentication on smartphones. In this paper, we present a novel multi-touch gesture-based authentication technique. We take advantage of the multi-touch surface to combine biometric techniques with gestural input. We defined a comprehensive set of five-finger touch gestures, based upon classifying movement characteristics of the center of the palm and fingertips, and tested them in a user study combining biometric data collection with usability questions. Using pattern recognition techniques, we built a classifier to recognize unique biometric gesture characteristics of an individual. We achieved a 90 accuracy rate with single gestures, and saw significant improvement when multiple gestures were performed in sequence. We found user ratings of a gestures desirable characteristics (ease, pleasure, excitement) correlated with a gestures actual biometric recognition rate - that is to say, user ratings aligned well with gestural security, in contrast to typical text-based passwords. Based on these results, we conclude that multi-touch gestures show great promise as an authentication mechanism. We propose an anonymous authentication protocol that supports time-bound credentials for an efficient revocation. It is especially suitable for large-scale network in roaming scenario. With our newly designed group signature scheme as a building block, a timestamp can be embedded to user secret key. No expired key can be used to authenticate, and hence naturally revoked users (e.g., due to contract expiration) are not required to be put into the revocation list. This makes our protocol much faster than previous roaming protocols in terms of revocation checking, which is a main part in verification. Current smartphones generally cannot continuously authenticate users during runtime. This poses severe security and privacy threats: A malicious user can manipulate the phone if bypassing the screen lock. To solve this problem, our work adopts a continuous and passive authentication mechanism based on a user’s touch operations on the touchscreen. Such a mechanism is suitable for smartphones, as it requires no extra hardware or intrusive user interface. We study how to model multiple types of touch data and perform continuous authentication accordingly. As a first attempt, we also investigate the fundamentals of touch operations as biometrics by justifying their distinctiveness and permanence. A onemonth experiment is conducted involving over 30 users. Our experiment results verify that touch biometrics can serve as a promising method for continuous and passive authentication. This paper considers signals from the cardiovascular system for possible use in biometric authentication. The signals of particular interest here derive from photoplethysmography (PPG), which refers to the use of illumination-based sensors that are sensitive to volumetric changes as blood travels through the body. Photoplethysmography sensors have been developed for the fingertip and the ear lobe, and they provide a convenient, noninvasive means of measuring heart rate and heart-rate variability. We demonstrate in this paper that PPG-based signals also have the potential to be used for biometric authentication, even though PPG signals appear to convey much less information than their electromagnetic counterparts, electrocardiograms (ECG). Through a novel decomposition into a sum-of-Gaussians representation, we present experimental results that indicate rank-1 accuracies of 90 and 95 with 2 seconds and 8 seconds of PPG test signal data, respectively. To our knowledge, this paper is the first to demonstrate robust PPG-based authentication for subjects with different emotional states. In this paper we propose a novel fusion protocol based on fuzzy fusion of face and voice features for checking liveness in secure identity authentication systems based on face and voice biometrics. Liveness checking can detect fraudulent impostor attacks on the security systems, and ensure that biometric cues are acquired from a live person who is actually present at the time of capture for authenticating the identity. The proposed fuzzy fusion of audio visual features is based on mutual dependency models which extract the spatio-temporal correlation between face and voice dynamics during speech production, Performance evaluation in terms of DET (Detector Error Tradeoff) curves and EERs (Equal Error Rates) on publicly available audiovisual speech databases show a significant improvement in performance of proposed fuzzy fusion of face-voice features based on mutual dependency models over conventional fusion techniques.