Doing good by fighting fraud: Ethical anti-fraud systems for mobile payments
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Abstract (arXiv)
App builders commonly use security challenges, a form of step-up authentication, to add security to their apps. However, the ethical implications of this type of architecture has not been studied previously. In this paper, we present a large-scale measurement study of running an existing anti-fraud security challenge, Boxer, in real apps running on mobile devices. We find that although Boxer does work well overall, it is unable to scan effectively on devices that run its machine learning models at less than one frame per second (FPS), blocking users who use inexpensive devices. With the insights from our study, we design Daredevil, anew anti-fraud system for scanning payment cards that work swell across the broad range of performance characteristics and hardware configurations found on modern mobile devices. Daredevil reduces the number of devices that run at less than one FPS by an order of magnitude compared to Boxer, providing a more equitable system for fighting fraud. In total, we collect data from 5,085,444 real devices spread across 496 real apps running production software and interacting with real users.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 54 references that are in the map. First posted on arXiv 28 Jun 2021.
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16 of 54 references.
References · 16
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4 more
- 2018Pelee: A Real-Time Object Detection System on Mobile Devicescited by 56
- 2018A First Look at Deep Learning Apps on Smartphonescited by 47
- 2019Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testingcited by 4
- 2019Characterizing the Deep Neural Networks Inference Performance of Mobile Applicationscited by 2
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