Biblio
Despite the additional protection it affords, two-factor authentication (2FA) adoption reportedly remains low. To better understand 2FA adoption and its barriers, we observed the deployment of a 2FA system at Carnegie Mellon University (CMU). We explore user behaviors and opinions around adoption, surrounding a mandatory adoption deadline. Our results show that (a) 2FA adopters found it annoying, but fairly easy to use, and believed it made their accounts more secure; (b) experience with CMU Duo often led to positive perceptions, sometimes translating into 2FA adoption for other accounts; and, (c) the differences between users required to adopt 2FA and those who adopted voluntarily are smaller than expected. We also explore the relationship between different usage patterns and perceived usability, and identify user misconceptions, insecure practices, and design issues. We conclude with recommendations for large-scale 2FA deployments to maximize adoption, focusing on implementation design, use of adoption mandates, and strategic messaging.
Machine learning is enabling a myriad innovations, including new algorithms for cancer diagnosis and self-driving cars. The broad use of machine learning makes it important to understand the extent to which machine-learning algorithms are subject to attack, particularly when used in applications where physical security or safety is at risk. In this paper, we focus on facial biometric systems, which are widely used in surveillance and access control. We define and investigate a novel class of attacks: attacks that are physically realizable and inconspicuous, and allow an attacker to evade recognition or impersonate another individual. We develop a systematic method to automatically generate such attacks, which are realized through printing a pair of eyeglass frames. When worn by the attacker whose image is supplied to a state-of-the-art face-recognition algorithm, the eyeglasses allow her to evade being recognized or to impersonate another individual. Our investigation focuses on white-box face-recognition systems, but we also demonstrate how similar techniques can be used in black-box scenarios, as well as to avoid face detection.