Authors: K Warren, T Tucker, A Crowder, D Olszewski
Year: 2024
Published in: Proceedings of the ..., 2024 - dl.acm.org
Institution: University of Florida
Research Area: Audio Deepfake Detection, Human Factors in AI Security, Perceptual Studies, AI Security
Discipline: Computer Science
Humans outperform machine learning models in classifying real human audio versus deepfakes, but are often misled by preconceptions about generated content, highlighting the need for more synergistic approaches between human and machine decision-making.
Methods: A large-scale user study was conducted where over 1,200 participants evaluated audio samples from three widely-cited deepfake datasets. Performance was quantitatively measured and thematic analysis was used to explore user reasoning and differences from machine classification.
Key Findings: Comparison of human and machine classification performance on audio deepfake detection, analysis of user reasoning, and evaluation of error patterns between both humans and models.
DOI: https://doi.org/10.1145/3658644.3670325
Citations: 14
Sample Size: 1200
