Deepfake detection by human crowds, machines, and machine-informed crowds

Matthew Groh*, Ziv Epstein, Chaz Firestone, Rosalind Picard

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

44 Scopus citations

Abstract

The recent emergence of machine-manipulated media raises an important societal question: How can we know whether a video that we watch is real or fake? In two online studies with 15,016 participants, we present authentic videos and deepfakes and ask participants to identify which is which. We compare the performance of ordinary human observers with the leading computer vision deepfake detection model and find them similarly accurate, while making different kinds of mistakes. Together, participants with access to the model’s prediction are more accurate than either alone, but inaccurate model predictions often decrease participants’ accuracy. To probe the relative strengths and weaknesses of humans and machines as detectors of deepfakes, we examine human and machine performance across video-level features, and we evaluate the impact of preregistered randomized interventions on deepfake detection. We find that manipulations designed to disrupt visual processing of faces hinder human participants’ performance while mostly not affecting the model’s performance, suggesting a role for specialized cognitive capacities in explaining human deepfake detection performance.

Original languageEnglish (US)
Article numbere2110013119
JournalProceedings of the National Academy of Sciences of the United States of America
Volume119
Issue number1
DOIs
StatePublished - Jan 4 2022

Keywords

  • Artificial intelligence
  • Facial recognition
  • Forensic science
  • Misinformation
  • Wisdom of crowds

ASJC Scopus subject areas

  • General

Fingerprint

Dive into the research topics of 'Deepfake detection by human crowds, machines, and machine-informed crowds'. Together they form a unique fingerprint.

Cite this