Does Cognitive Load Affect Human Accuracy in Detecting Voice-Based Deepfakes?

TL;DR

This study examines how cognitive load influences human ability to detect voice deepfakes, revealing that low load does not impair and may even enhance detection performance.

Contribution

It provides empirical evidence on the impact of cognitive load on voice deepfake detection, addressing a gap in realistic social media scenarios.

Findings

Low cognitive load does not impair detection accuracy.

Simultaneous secondary stimuli can improve detection performance.

Detection abilities are resilient under certain cognitive conditions.

Abstract

Deepfake technologies are powerful tools that can be misused for malicious purposes such as spreading disinformation on social media. The effectiveness of such malicious applications depends on the ability of deepfakes to deceive their audience. Therefore, researchers have investigated human abilities to detect deepfakes in various studies. However, most of these studies were conducted with participants who focused exclusively on the detection task; hence the studies may not provide a complete picture of human abilities to detect deepfakes under realistic conditions: Social media users are exposed to cognitive load on the platform, which can impair their detection abilities. In this paper, we investigate the influence of cognitive load on human detection abilities of voice-based deepfakes in an empirical study with 30 participants. Our results suggest that low cognitive load does not generally impair detection abilities, and that the simultaneous exposure to a secondary stimulus can actually benefit people in the detection task.