CocktailAuth: Auditory Perception-Driven Authentication Based on the Cocktail Party Effect

When a specific voice rises above the cacophony of a crowded room, how does your perceptual system filter out competing sounds and turn toward that single talker? In this paper, we first conduct a formative study examining this Cocktail Party Effect, uncovering that the closed-loop auditory-perception-response patterns are significant, discriminable, and serve as a unique biometric signature. Based on this observation, we present CocktailAuth, a novel authentication prototype for head-worn devices, implemented and evaluated on commercial VR platforms. Unlike existing methods that typically rely on disruptive explicit inputs, vulnerable static biometrics, or shallow external motion patterns, CocktailAuth exploits the deep-seated, perception-driven head movements naturally exhibited during selective listening. We design four auditory scenarios to elicit these perceptual responses and construct a comprehensive feature framework characterizing both static statistical descriptors and dynamic time series. We evaluate CocktailAuth with a VR prototype in a study of 50 participants. Under a leave-one-session-out evaluation protocol, CocktailAuth achieves an EER of 4.64% and a BAC of 95.36%, and further improves to an EER of 2.07% and a BAC of 97.98% when aggregating multiple samples. By leveraging these auditory perception-driven mechanisms, CocktailAuth offers strong resistance to mimicry attacks and provides a natural, secure, and hardware-friendly solution for ubiquitous wearable devices.