3 papers
cs.CV2026
Human-AI Ensembles Improve Deepfake Detection in Low-to-Medium Quality Videos
Marco Postiglione, Isabel Gortner, V. S. Subrahmanian
Deepfake detection is widely framed as a machine learning problem, yet how humans and AI detectors compare under realistic conditions remains poorly understood. We evaluate 200 hum…
cs.AI2026
Context and Transcripts Improve Detection of Deepfake Audios of Public Figures
Chongyang Gao, Marco Postiglione, Julian Baldwin +6
Humans use context to assess the veracity of information. However, current audio deepfake detectors only analyze the audio file without considering either context or transcripts. W…
cs.SD2025
Perturbed Public Voices (PV): A Dataset for Robust Audio Deepfake Detection
Chongyang Gao, Marco Postiglione, Isabel Gortner +2
Current audio deepfake detectors cannot be trusted. While they excel on controlled benchmarks, they fail when tested in the real world. We introduce Perturbed Public Voices (P$^{2}…