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App builders commonly use security challenges, a form of step-upauthentication, to add security to their apps. However, the ethicalimplications of this type of architecture has not been studied previously. Inthis paper, we present a large-scale measurement study of running an existinganti-fraud security challenge, Boxer, in real apps running on mobile devices.We find that although Boxer does work well overall, it is unable to scaneffectively on devices that run its machine learning models at less than oneframe per second (FPS), blocking users who use inexpensive devices. With theinsights from our study, we design Daredevil, anew anti-fraud system forscanning payment cards that work swell across the broad range of performancecharacteristics and hardware configurations found on modern mobile devices.Daredevil reduces the number of devices that run at less than one FPS by anorder of magnitude compared to Boxer, providing a more equitable system forfighting fraud. In total, we collect data from 5,085,444 real devices spreadacross 496 real apps running production software and interacting with realusers.

Doing good by fighting fraud: Ethical anti-fraud systems for mobile payments