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.
@cite_42 found that creating and entering passwords on mobile devices take longer than desktops and laptops. In mobile devices, text-based passwords need to be entered on spatially limited keyboards on which typing a single character may require multiple touches @cite_57 , due also to typing the wrong key. Pixie replaces typing a password with pointing the camera to the trinket and snapping a photo of it.
@cite_57 @cite_42
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Recent research has improved our understanding of how to create strong, memorable text passwords. However, this research has generally been in the context of desktops and laptops, while users are increasingly creating and entering passwords on mobile devices. In this paper we study whether recent password guidance carries over to the mobile setting. We compare the strength and usability of passwords created and used on mobile devices with those created and used on desktops and laptops, while varying password policy requirements and input methods. We find that creating passwords on mobile devices takes significantly longer and is more error prone and frustrating. Passwords created on mobile devices are also weaker, but only against attackers who can make more than 10^13 guesses. We find that the effects of password policies differ between the desktop and mobile environments, and suggest ways to ease password entry for mobile users. With the rich functionalities and enhanced computing capabilities available on mobile computing devices with touch screens, users not only store sensitive information (such as credit card numbers) but also use privacy sensitive applications (such as online banking) on these devices, which make them hot targets for hackers and thieves. To protect private information, such devices typically lock themselves after a few minutes of inactivity and prompt a password PIN pattern screen when reactivated. Passwords PINs patterns based schemes are inherently vulnerable to shoulder surfing attacks and smudge attacks. Furthermore, passwords PINs patterns are inconvenient for users to enter frequently. In this paper, we propose GEAT, a gesture based user authentication scheme for the secure unlocking of touch screen devices. Unlike existing authentication schemes for touch screen devices, which use what user inputs as the authentication secret, GEAT authenticates users mainly based on how they input, using distinguishing features such as finger velocity, device acceleration, and stroke time. Even if attackers see what gesture a user performs, they cannot reproduce the behavior of the user doing gestures through shoulder surfing or smudge attacks. We implemented GEAT on Samsung Focus running Windows, collected 15009 gesture samples from 50 volunteers, and conducted real-world experiments to evaluate GEAT's performance. Experimental results show that our scheme achieves an average equal error rate of 0.5 with 3 gestures using only 25 training samples. Password-based authentication is easy to use but its security is bounded by how much a user can remember. Biometrics-based authentication requires no memorization but ‘resetting’ a biometric password may not always be possible. In this paper, we propose a user-friendly authentication system (KinWrite) that allows users to choose arbitrary, short and easy-to-memorize passwords while providing resilience to password cracking and password theft. KinWrite lets users write their passwords in 3D space and captures the handwriting motion using a low cost motion input sensing device—Kinect. The low resolution and noisy data captured by Kinect, combined with low consistency of in-space handwriting, have made it challenging to verify users. To overcome these challenges, we exploit the Dynamic Time Warping (DTW) algorithm to quantify similarities between handwritten passwords. Our experimental results involving 35 signatures from 18 subjects and a brute-force attacker have shown that KinWrite can achieve a 100 precision and a 70 recall (the worst case) for verifying honest users, encouraging us to carry out a much larger scale study towards designing a foolproof system. Mobile devices gain increasing computational power and storage capabilities, and there are already mobile phones that can show movies, act as digital music players and offer full-scale web browsing. The bottleneck for information flow is however limited by the inefficient communication channel between the user and the small device. The small mobile phone form factor has proven to be surprisingly difficult to overcome and limited text entry capabilities are in effect crippling mobile devices’ use experience. The desktop keyboard is too large for mobile phones, and the keypad too limited. In recent years, advanced mobile phones have come equipped with touch-screens that enable new text entry solutions. This dissertation explores how software keyboards on touch-screens can be improved to provide an efficient and practical text and command entry experience on mobile devices. The central hypothesis is that it is possible to combine three elements: software keyboard, language redundancy and pattern recognition, and create new effective interfaces for text entry and control. These are collectively called “shape writing” interfaces. Words form shapes on the software keyboard layout. Users write words by articulating the shapes for words on the software keyboard. Two classes of shape writing interfaces are developed and analyzed: discrete and continuous shape writing. The former recognizes users’ pen or finger tapping motion as discrete patterns on the touch-screen. The latter recognizes users’ continuous motion patterns. Experimental results show that novice users can write text with an average entry rate of 25 wpm and an error rate of 1 after 35 minutes of practice. An accelerated novice learning experiment shows that users can exactly copy a single well-practiced phrase with an average entry rate of 46.5 wpm, with individual phrase entry rate measurements up to 99 wpm. When used as a control interface, users can send commands to applications 1.6 times faster than using de-facto standard linear pull-down menus. Visual command preview leads to significantly less errors and shorter gestures for unpracticed commands. Taken together, the quantitative results show that shape writing is among the fastest mobile interfaces for text entry and control, both initially and after practice, that are currently known. Authentication methods can be improved by considering implicit, individual behavioural cues. In particular, verifying users based on typing behaviour has been widely studied with physical keyboards. On mobile touchscreens, the same concepts have been applied with little adaptations so far. This paper presents the first reported study on mobile keystroke biometrics which compares touch-specific features between three different hand postures and evaluation schemes. Based on 20.160 password entries from a study with 28 participants over two weeks, we show that including spatial touch features reduces implicit authentication equal error rates (EER) by 26.4 - 36.8 relative to the previously used temporal features. We also show that authentication works better for some hand postures than others. To improve applicability and usability, we further quantify the influence of common evaluation assumptions: known attacker data, training and testing on data from a single typing session, and fixed hand postures. We show that these practices can lead to overly optimistic evaluations. In consequence, we describe evaluation recommendations, a probabilistic framework to handle unknown hand postures, and ideas for further improvements. Shoulder-surfing -- using direct observation techniques, such as looking over someone's shoulder, to get passwords, PINs and other sensitive personal information -- is a problem that has been difficult to overcome. When a user enters information using a keyboard, mouse, touch screen or any traditional input device, a malicious observer may be able to acquire the user's password credentials. We present EyePassword, a system that mitigates the issues of shoulder surfing via a novel approach to user input. With EyePassword, a user enters sensitive input (password, PIN, etc.) by selecting from an on-screen keyboard using only the orientation of their pupils (i.e. the position of their gaze on screen), making eavesdropping by a malicious observer largely impractical. We present a number of design choices and discuss their effect on usability and security. We conducted user studies to evaluate the speed, accuracy and user acceptance of our approach. Our results demonstrate that gaze-based password entry requires marginal additional time over using a keyboard, error rates are similar to those of using a keyboard and subjects preferred the gaze-based password entry approach over traditional methods. The underlying issues relating to the usability and security of multiple passwords are largely unexplored. However, we know that people generally have difficulty remembering multiple passwords. This reduces security since users reuse the same password for different systems or reveal other passwords as they try to log in. We report on a laboratory study comparing recall of multiple text passwords with recall of multiple click-based graphical passwords. In a one-hour session (short-term), we found that participants in the graphical password condition coped significantly better than those in the text password condition. In particular, they made fewer errors when recalling their passwords, did not resort to creating passwords directly related to account names, and did not use similar passwords across multiple accounts. After two weeks, participants in the two conditions had recall success rates that were not statistically different from each other, but those with text passwords made more recall errors than participants with graphical passwords. In our study, click-based graphical passwords were significantly less susceptible to multiple password interference in the short-term, while having comparable usability to text passwords in most other respects. We study the performance and user experience of two popular mainstream mobile text entry methods: the Smart Touch Keyboard (STK) and the Smart Gesture Keyboard (SGK). Our first study is a lab-based ten-session text entry experiment. In our second study we use a new text entry evaluation methodology based on the experience sampling method (ESM). In the ESM study, participants installed an Android app on their own mobile phones that periodically sampled their text entry performance and user experience amid their everyday activities for four weeks. The studies show that text can be entered at an average speed of 28 to 39 WPM, depending on the method and the user's experience, with 1.0 to 3.6 character error rates remaining. Error rates of touchscreen input, particularly with SGK, are a major challenge; and reducing out-of-vocabulary errors is particularly important. Both SGK and STK have strengths, weaknesses, and different individual awareness and preferences. Two-thumb touch typing in a focused setting is particularly effective on STK, whereas one-handed SGK typing with the thumb is particularly effective in more mobile situations. When exposed to both, users tend to migrate from STK to SGK. We also conclude that studies in the lab and in the wild can both be informative to reveal different aspects of keyboard experience, but used in conjunction is more reliable in comprehensively assessing input technologies of current and future generations. Free-form gesture passwords have been introduced as an alternative mobile authentication method. Text passwords are not very suitable for mobile interaction, and methods such as PINs and grid patterns sacrifice security over usability. However, little is known about how free-form gestures perform in the wild. We present the first field study (N=91) of mobile authentication using free-form gestures, with text passwords as a baseline. Our study leveraged Experience Sampling Methodology to increase ecological validity while maintaining control of the experiment. We found that, with gesture passwords, participants generated new passwords and authenticated faster with comparable memorability while being more willing to retry. Our analysis of the gesture password dataset indicated biases in user-chosen distribution tending towards common shapes. Our findings provide useful insights towards understanding mobile device authentication and gesture-based authentication. Securing the sensitive data stored and accessed from mobile devices makes user authentication a problem of paramount importance. The tension between security and usability renders however the task of user authentication on mobile devices a challenging task. This paper introduces FAST (Fingergestures Authentication System using Touchscreen), a novel touchscreen based authentication approach on mobile devices. Besides extracting touch data from touchscreen equipped smartphones, FAST complements and validates this data using a digital sensor glove that we have built using off-the-shelf components. FAST leverages state-of-the-art classification algorithms to provide transparent and continuous mobile system protection. A notable feature is FAST 's continuous, user transparent post-login authentication. We use touch data collected from 40 users to show that FAST achieves a False Accept Rate (FAR) of 4.66 and False Reject Rate of 0.13 for the continuous post-login user authentication. The low FAR and FRR values indicate that FAST provides excellent post-login access security, without disturbing the honest mobile users. 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. KeyScretch is a text entry method for devices equipped with touch-screens, based on a menu-augmented soft keyboard. In these keyboards, a menu containing a small number of frequent characters is shown, while a key is pressed, allowing further character entry by menu selection. KeyScretch improves the previously studied menu-based methods by enabling the interpretation of compound strokes, which allow the input of text chunks longer than two characters. The performance of the method is analyzed on different kinds of touch-screens: First, we present a 25-session user study on a stylus-based device, showing that an instance of the method optimized for Italian can be learned in a reasonable time by the users and significantly outperforms the traditional method based on the tapping interaction. Then, we define and validate a model for predicting expert text entry rates on finger-based devices. The predicted rates for instances of KeyScretch optimized for different Western languages vary from about 44-50 words min on the Qwerty layout, enabling improvements in the range of 30-49 as compared with the traditional method. Mobile handsets have found an important place in modern society, with hundreds of millions currently in use. The majority of these devices use inherently weak authentication mechanisms, based upon passwords and PINs. This paper presents a feasibility study into a biometric-based technique, known as keystroke analysis – which authenticates the user based upon their typing characteristic. In particular, this paper identifies two typical handset interactions, entering telephone numbers and typing text messages, and seeks to authenticate the user during their normal handset interaction. It was found that neural network classifiers were able to perform classification with average equal error rates of 12.8 . Based upon these results, the paper concludes by proposing a flexible and robust framework to permit the continuous and transparent authentication of the user, thereby maximising security and minimising user inconvenience, to service the needs of the insecure and evermore functional mobile handset. Eyes-free input is desirable for ubiquitous computing, since interacting with mobile and wearable devices often competes for visual attention with other devices and tasks. In this paper, we explore eyes-free typing on a touchpad using one thumb, wherein a user taps on an imaginary QWERTY keyboard while receiving text feedback on a separate screen. Our hypothesis is that users can transfer their typing ability obtained from visible keyboards to eyes-free use. We propose two statistical decoding algorithms to infer users’ eyes-free input: the absolute algorithm and the relative algorithm. The absolute algorithm infers user input based on the absolute position of touch endpoints, while the relative algorithm infers based on the vectors between successive touch endpoints. Evaluation results showed users could achieve satisfying performance with both algorithms. Text entry rate was 17-23 WPM (words per minute) depending on the algorithm used. In comparison, a baseline cursor-based text entry method yielded only 7.66 WPM. In conclusion, our research demonstrates for the first time the feasibility of thumb-based eyes-free typing, which provides a new possibility for text entry on ubiquitous computing platforms such as smart TVs and HMDs. The most common CAPTCHA technique, Gimpy which requires a user to type in twisted characters has following problems. Characters are over twisted because some spammer uses OCR to bypass verification. Type in operation on mobile and tablet devices is difficult where keyboard are not utilized. Our method uses multiple noise images where invisible objects or message is hidden. The hidden objects appear when two images are overlapped at certain position. Finding the point is a key to pass verification which is difficult for none human intelligence. This enhances usability on tablet devices since a user only has to use one finger to get verified instead of using a keyboard. Abstract Typing on tiny QWERTY keyboards on smartwatches is considered challenging or even impractical due to the limited screen space. In this paper, we describe three user studies undertaken to investigate users’ typing abilities and preferences on tiny QWERTY keyboards. The first two studies, using a smartphone as a substitute for a smartwatch, tested five different keyboard sizes (2, 2.5, 3, 3.5 and 4 cm). Study 1 collected typing data from participants using keyboards and given asterisk feedback. We analyzed both the distribution of touch points (e.g., the systematic offset and shape of the distribution) and the effect of keyboard size. Study 2 adopted a Bayesian algorithm based on a touch model derived from Study 1 and a unigram word language model to perform input prediction. We found that on the smart keyboard, participants could type between 26.8 and 33.6 words per minute (WPM) across the five keyboard sizes with an uncorrected character error rate ranging from 0.4 to 1.9 . Participants’ subjective feedback indicated that they felt most comfortable with keyboards larger than 2.5 cm. Study 3 replicated the 3.0 and 3.5 cm keyboard tests on a real smartwatch and verified that in terms of text entry speed, error rate and user preference, there was no significant difference between the results measured on a smartphone and that on a smartwatch with same sized keys. This study result indicated that the results of Study 1 and 2 are applicable to smartwatch devices. Finally, we conducted a simulation to investigate the performance of different touch language models based on our collected data. The results showed that using either a bigram language model or a detailed touch model can effectively correct imprecision in users’ input. Our results suggest that achieving satisfactory levels of text input on tiny QWERTY keyboards is possible. Touch typing on flat surfaces (e.g. interactive tabletop) is challenging due to lack of tactile feedback and hand drifting. In this paper, we present TOAST, an eyes-free keyboard technique for enabling efficient touch typing on touch-sensitive surfaces. We first formalized the problem of keyboard parameter (e.g. location and size) estimation based on users' typing data. Through a user study, we then examined users' eyes-free touch typing behavior on an interactive tabletop with only asterisk feedback. We fitted the keyboard model to the typing data, results suggested that the model parameters (keyboard location and size) changed not only between different users, but also within the same user along with time. Based on the results, we proposed a Markov-Bayesian algorithm for input prediction, which considers the relative location between successive touch points within each hand respectively. Simulation results showed that based on the pooled data from all users, this model improved the top-1 accuracy of the classical statistical decoding algorithm from 86.2 to 92.1 . In a second user study, we further improved TOAST with dynamical model parameter adaptation, and evaluated users' text entry performance with TOAST using realistic text entry tasks. Participants reached a pick-up speed of 41.4 WPM with a character-level error rate of 0.6 . And with less than 10 minutes of practice, they reached 44.6 WPM without sacrificing accuracy. Participants' subjective feedback also indicated that TOAST offered a natural and efficient typing experience. Today's Internet services rely heavily on text-based passwords for user authentication. The pervasiveness of these services coupled with the difficulty of remembering large numbers of secure passwords tempts users to reuse passwords at multiple sites. In this paper, we investigate for the first time how an attacker can leverage a known password from one site to more easily guess that user's password at other sites. We study several hundred thousand leaked passwords from eleven web sites and conduct a user survey on password reuse; we estimate that 43- 51 of users reuse the same password across multiple sites. We further identify a few simple tricks users often employ to transform a basic password between sites which can be used by an attacker to make password guessing vastly easier. We develop the first cross-site password-guessing algorithm, which is able to guess 30 of transformed passwords within 100 attempts compared to just 14 for a standard password-guessing algorithm without cross-site password knowledge. In this paper, we propose and evaluate Use Your Illusion, a novel mechanism for user authentication that is secure and usable regardless of the size of the device on which it is used. Our system relies on the human ability to recognize a degraded version of a previously seen image. We illustrate how distorted images can be used to maintain the usability of graphical password schemes while making them more resilient to social engineering or observation attacks. Because it is difficult to mentally "revert" a degraded image, without knowledge of the original image, our scheme provides a strong line of defense against impostor access, while preserving the desirable memorability properties of graphical password schemes. Using low-fidelity tests to aid in the design, we implement prototypes of Use Your Illusion as i) an Ajax-based web service and ii) on Nokia N70 cellular phones. We conduct a between-subjects usability study of the cellular phone prototype with a total of 99 participants in two experiments. We demonstrate that, regardless of their age or gender, users are very skilled at recognizing degraded versions of self-chosen images, even on small displays and after time periods of one month. Our results indicate that graphical passwords with distorted images can achieve equivalent error rates to those using traditional images, but only when the original image is known. We take a closer look at keyboard acoustic emanations specifically for the purpose of eavesdropping over random passwords. In this scenario, dictionary and HMM language models are not applicable; the attacker can only utilize the raw acoustic information which has been recorded. We investigate several existing signal processing techniques for our purpose, and introduce a novel technique -- time-frequency decoding -- that improves the detection accuracy compared to previous techniques. We also carefully examine the effect of typing style -- a crucial variable largely ignored by prior research -- on the detection accuracy. Our results show that using the same typing style (hunt and peck) for both training and decoding the data, the best case success rate for detecting correctly the typed key is 64 per character. The results also show that changing the typing style, to touch typing, during the decoding stage reduces the success rate, but using the time-frequency technique, we can still achieve a success rate of around 40 per character. Our work takes the keyboard acoustic attack one step further, bringing it closer to a full-fledged vulnerability under realistic scenarios (different typing styles and random passwords). Our results suggest that while the performance of these attacks degrades under such conditions, it is still possible, utilizing the time-frequency technique, to considerably reduce the exhaustive search complexity of retrieving a random password.