（盲）用户确实会留意语音电话诈骗警告
(Blind) Users Really Do Heed Aural Telephone Scam Warnings
AI总结：
研究通过冷呼叫实验比较盲人与视力正常用户对语音诈骗警告的反应，证明诈骗内容前的情境警告能有效降低受骗风险，并提出无障碍实现与隐私建议。
摘要：
本文报告了一项研究，探讨两组个体——法定盲人（n=36）和视力正常者（n=36）——在自然场景中如何对语音电话诈骗警告作出反应。由于伪造 CallerID 非常容易，转而传达来电的背景信息，为向接听者警示潜在诈骗提供了更好的可能。通常，此类警告本质上是视觉性的，无法照顾有视觉障碍的用户。为解决这种排斥，我们开发了电话诈骗警告的语音变体，并在三种条件下进行测试：基线（无警告）、简短警告，以及在诈骗内容之前播放的情境警告。我们通过冷呼叫参与者并记录其行为，随后进行情况说明并获取同意，测试了两种最常见的诈骗场景：欺诈（降低利率）和身份盗窃（社会安全号码）。只有两名参与者按照诈骗要求“按 1”，且均来自听到社会安全号码场景情境警告的法定盲人组。经过仔细核查，我们了解到其中一人这样做是因为其屏幕阅读器存在无障碍问题，另一人则是故意为之，因为警告说服了她去浪费诈骗者的时间，使他们无法诈骗弱势群体。法定盲参与者和视力正常参与者均认为，情境警告是强有力的可用安全线索；它们与“Scam Likely”等 STIR/SHAKEN 指标相结合，将针对任何类型的诈骗提供稳健保护。我们还讨论了情境警告的潜在隐私影响，并收集了关于可用且无障碍实现的建议。
英文摘要：
This paper reports on a study exploring how two groups of individuals, legally blind (n=36) and sighted ones (n=36), react to aural telephone scam warnings in naturalistic settings. As spoofing a CallerID is trivial, communicating the context of an incoming call instead offers a better possibility to warn a receiver about a potential scam. Usually, such warnings are visual in nature and fail to cater to users with visual disabilities. To address this exclusion, we developed an aural variant of telephone scam warnings and tested them in three conditions: baseline (no warning), short warning, and contextual warning that preceded the scam's content. We tested the two most common scam scenarios: fraud (interest rate reduction) and identity theft (social security number) by cold-calling participants and recording their action, and debriefing and obtaining consent afterward. Only two participants "pressed one" as the scam demanded, both from the legally blind group that heard the contextual warning for the social security scenario. Upon close inspection, we learned that one of them did so because of accessibility issues with their screen reader and the other did so intentionally because the warning convinced them to waste the scammer's time, so they don't scam vulnerable people. Both the legally blind and the sighted participants found the contextual warnings as powerful usable security cues that, together with STIR/SHAKEN indicators like "Scam Likely", would provide robust protection against any type of scam. We also discussed the potential privacy implications of the contextual warnings and collected recommendations for usably accessible implementation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
