Biblio

Images perturbed subtly to be misclassified by neural networks, called adversarial examples, have emerged as a technically deep challenge and an important concern for several application domains. Most research on adversarial examples takes as its only constraint that the perturbed images are similar to the originals. However, real-world application of these ideas often requires the examples to satisfy additional objectives, which are typically enforced through custom modifications of the perturbation process. In this article, we propose adversarial generative nets (AGNs), a general methodology to train a generator neural network to emit adversarial examples satisfying desired objectives. We demonstrate the ability of AGNs to accommodate a wide range of objectives, including imprecise ones difficult to model, in two application domains. In particular, we demonstrate physical adversarial examples—eyeglass frames designed to fool face recognition—with better robustness, inconspicuousness, and scalability than previous approaches, as well as a new attack to fool a handwritten-digit classifier.

Security experts often recommend using password-management tools that both store passwords and generate random passwords. However, research indicates that only a small fraction of users use password managers with password generators. Past studies have explored factors in the adoption of password managers using surveys and online store reviews. Here we describe a semi-structured interview study with 30 participants that allows us to provide a more comprehensive picture of the mindsets underlying adoption and effective use of password managers and password-generation features. Our participants include users who use no password-specific tools at all, those who use password managers built into browsers or operating systems, and those who use separately installed password managers. Furthermore, past field data has indicated that users of built-in, browser-based password managers more often use weak and reused passwords than users of separate password managers that have password generation available by default. Our interviews suggest that users of built-in password managers may be driven more by convenience, while users of separately installed tools appear more driven by security. We advocate tailored designs for these two mentalities and provide actionable suggestions to induce effective password manager usage.

Many authentication schemes ask users to manually compare compact representations of cryptographic keys, known as fingerprints. If the fingerprints do not match, that may signal a man-in-the-middle attack. An adversary performing an attack may use a fingerprint that is similar to the target fingerprint, but not an exact match, to try to fool inattentive users. Fingerprint representations should thus be both usable and secure. We tested the usability and security of eight fingerprint representations under different configurations. In a 661-participant between-subjects experiment, participants compared fingerprints under realistic conditions and were subjected to a simulated attack. The best configuration allowed attacks to succeed 6% of the time; the worst 72%. We find the seemingly effective compare-and-select approach performs poorly for key fingerprints and that graphical fingerprint representations, while intuitive and fast, vary in performance. We identify some fingerprint representations as particularly promising.

Risk homeostasis theory claims that individuals adjust their behaviors in response to changing variables to keep what they perceive as a constant accepted level of risk [8]. Risk homeostasis theory is used to explain why drivers may drive faster when wearing seatbelts. Here we explore whether risk homeostasis theory applies to end-user security behaviors. We use observed data from over 200 participants in a longitudinal in-situ study as well as survey data from 249 users to attempt to determine how user security behaviors and attitudes are affected by the presence or absence of antivirus software. If risk compensation is occurring, users might be expected to behave more dangerously in some ways when antivirus is present. Some of our preliminary data suggests that risk compensation may be occurring, but additional work with larger samples is needed. 

To help users create stronger text-based passwords, many web sites have deployed password meters that provide visual feedback on password strength. Although these meters are in wide use, their effects on the security and usability of passwords have not been well studied. We present a 2,931-subject study of password creation in the presence of 14 password meters. We found that meters with a variety of visual appearances led users to create longer passwords. However, significant increases in resistance to a password-cracking algorithm were only achieved using meters that scored passwords stringently. These stringent meters also led participants to include more digits, symbols, and uppercase letters. Password meters also affected the act of password creation. Participants who saw stringent meters spent longer creating their password and were more likely to change their password while entering it, yet they were also more likely to find the password meter annoying. However, the most stringent meter and those without visual bars caused participants to place less importance on satisfying the meter. Participants who saw more lenient meters tried to fill the meter and were averse to choosing passwords a meter deemed “bad” or “poor.” Our findings can serve as guidelines for administrators seeking to nudge users towards stronger passwords.