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.
Pixie's visual nature is similar to graphical passwords, that include recall, recognition and cued recall systems (see @cite_77 for a survey). Recall based solutions such as DAS (Draw-A-Secret) @cite_28 and variants @cite_70 @cite_11 ask the user to enter their password using a stylus, mouse or finger. For instance, De @cite_72 proposed to enter the stroke based password on the front or back of a double sided touch screen device. In recognition-based systems (e.g., Passfaces @cite_33 @cite_67 ), users create a password by selecting and memorizing a set of images (e.g., faces), which they need to recognize from among other images during the authentication process.
@cite_67 @cite_33 @cite_28 @cite_70 @cite_77 @cite_72 @cite_11
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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. 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. 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. Current secure systems suffer because they neglect the importance of human factors in security. We address a fundamental weakness of knowledge-based authentication schemes, which is the human limitation to remember secure passwords. Our approach to improve the security of these systems relies on recognition-based, rather than recall-based authentication. We examine the requirements of a recognition-based authentication system and propose Deja Vu, which authenticates a user through her ability to recognize previously seen images. Deja Vu is more reliable and easier to use than traditional recall-based schemes, which require the user to precisely recall passwords or PINs. Furthermore, it has the advantage that it prevents users from choosing weak passwords and makes it difficult to write down or share passwords with others. We develop a prototype of Deja Vu and conduct a user study that compares it to traditional password and PIN authentication. Our user study shows that 90 of all participants succeeded in the authentication tests using Deja Vu while only about 70 succeeded using passwords and PINS. Our findings indicate that Deja Vu has potential applications, especially where text input is hard (e.g., PDAs or ATMs), or in situations where passwords are infrequently used (e.g., web site passwords). It is an interesting problem how a human can prove its identity to a trustworthy (local or remote) computer with untrustworthy input devices and via an insecure channel controlled by adversaries. Any input devices and auxiliary devices are untrustworthy under the following assumptions: the adversaries can record humans’ operations on the devices, and can access the devices to replay the recorded operations. Strictly, only the common brain intelligence is available for the human. In this paper, such an identication system is called SecHCI as the abbreviation Secure Human-Computer Identication (or Interface). In the real world, SecHCI means the peeping attacks to widely-used xed passwords: an adversary can observe your password via his own eyes or some hidden device (such as min-camera) when your input them on your keyboard or with your mouse. Compared with human-computer identications with the aid of trustworthy hardware devices, only a few contributions have devoted to the design and analysis of SecHCI. The most systematic works are made by N. J. Hopper & M. Blum recently: some formal denitions are given and the feasibility is shown by several SecHCI protocols with acceptable security (but usability is not very good because of their inherent limitations). In this paper, we give comprehensive investigations on SecHCI, from both theoretical and practical viewpoint, and with both system-oriented and usercentered methods. A user study is made to show problems of xed passwords, the signicance of peeping attack and some design principles of human-computer identications. All currently known SecHCI protocols and some related works (such as visual graphical passwords and CAPTCHAs) are surveyed in detail. In addition, we also give our opinions on future research and suggest a new prototype protocol as a possible solution to this problem. Draw a secret (DAS) is a representative graphical password scheme. Rigorous theoretical analysis suggests that DAS supports an overall password space larger than that of the ubiquitous textual password scheme. However, recent research suggests that DAS users tend to choose weak passwords, and their choices would render this theoretically sound scheme less secure in real life. In this paper we investigate the novel idea of introducing background images to the DAS scheme, where users were initially supposed to draw passwords on a blank canvas overlaid with a grid. Encouraging results from our two user studies have shown that people aided with background images tended to set significantly more complicated passwords than their counterparts using the original scheme. The background images also reduced other predictable characteristics in DAS passwords such as symmetry and centering within the drawing grid, further improving the strength of the passwords. We estimate that the average strength of successfully recalled passwords in the enhanced scheme was increased over those created using the original scheme by more than 10 bits. Moreover, a positive effect was observed with respect to the memorability of the more complex passwords encouraged by the background images. Graphical passwords are often considered prone to shoulder-surfing attacks, where attackers can steal a user's password by peeking over his or her shoulder in the authentication process. In this paper, we explore shoulder surfing defence for recall-based graphical password systems such as Draw-A-Secret and Background Draw-A-Secret, where users doodle their passwords (i.e. secrets) on a drawing grid. We propose three innovative shoulder surfing defence techniques, and conduct two separate controlled laboratory experiments to evaluate both security and usability perspectives of the proposed techniques. One technique was expected to work to some extent theoretically, but it turned out to provide little protection. One technique provided the best overall shoulder surfing defence, but also caused some usability challenges. The other technique achieved reasonable shoulder surfing defence and good usability simultaneously, a good balance which the two other techniques did not achieve. Our results appear to be also relevant to other graphical password systems such as Pass-Go.