Dongnanzi Zheng

Antonios Saravanos, Dongnanzi Zheng, Stavros Zervoudakis et al.

Locimetric authentication is a form of graphical authentication in which users validate their identity by selecting predetermined points on a predetermined image. Its primary advantage over the ubiquitous text-based approach stems from users' superior ability to remember visual information over textual information, coupled with the authentication process being transformed to one requiring recognition (instead of recall). Ideally, these differentiations enable users to create more complex passwords, which theoretically are more secure. Yet locimetric authentication has one significant weakness: hot-spots. This term refers to areas of an image that users gravitate towards, and which consequently have a higher probability of being selected. Although many strategies have been proposed to counter the hot-spot problem, one area that has received little attention is that of resolution. The hypothesis here is that high-resolution images would afford the user a larger password space, and consequently any hot-spots would dissipate. We employ an experimental approach, where users generate a series of locimetric passwords on either low- or high-resolution images. Our research reveals the presence of hot-spots even in high-resolution images, albeit at a lower level than that exhibited with low-resolution images. We conclude by reinforcing that other techniques - such as existing or new software controls or training - need to be utilized to mitigate the emergence of hot-spots with the locimetric scheme.

Antonios Saravanos, Stavros Zervoudakis, Dongnanzi Zheng et al.

In this study, we investigate the attentiveness exhibited by participants sourced through Amazon Mechanical Turk (MTurk), thereby discovering a significant level of inattentiveness amongst the platform's top crowd workers (those classified as 'Master', with an 'Approval Rate' of 98% or more, and a 'Number of HITS approved' value of 1,000 or more). A total of 564 individuals from the United States participated in our experiment. They were asked to read a vignette outlining one of four hypothetical technology products and then complete a related survey. Three forms of attention check (logic, honesty, and time) were used to assess attentiveness. Through this experiment we determined that a total of 126 (22.3%) participants failed at least one of the three forms of attention check, with most (94) failing the honesty check - followed by the logic check (31), and the time check (27). Thus, we established that significant levels of inattentiveness exist even among the most elite MTurk workers. The study concludes by reaffirming the need for multiple forms of carefully crafted attention checks, irrespective of whether participant quality is presumed to be high according to MTurk criteria such as 'Master', 'Approval Rate', and 'Number of HITS approved'. Furthermore, we propose that researchers adjust their proposals to account for the effort and costs required to address participant inattentiveness.