Providing detection strategies to improve human detection of deepfakes
An experimental study
Bibliographic Data
| ID | 21563589 |
|---|---|
| Authors | Klaire Somoray (0000-0001-7521-1425, James Cook University, corresponding author), Dan J Miller (0000-0002-3230-2631, James Cook University) |
| Year | 2023 |
| Volume | 149 |
| Pages | 107917 |
| Publication date | 2023-12-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Computers in Human Behavior (JOURNAL) |
| Journal identifiers | ISSN: 0747-5632 • E-ISSN: 1873-7692 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.chb.2023.107917 |
| OpenAlex | W4386034004 |
| Language | EN |
| Citations received | 10 |
| References cited | 15 |
Deepfake videos are becoming more pervasive. In this preregistered online experiment, participants (N = 454, Mage = 37.19, SDage = 13.25, males = 57.5%) categorize a series of 20 videos as either real or deepfake. All participants saw 10 real and 10 deepfake videos. Participants were randomly assigned to receive a list of strategies for detecting deepfakes based on visual cues (e.g., looking for common artifacts such as skin smoothness) or to act as a control group. Participants were also asked how confident they were that they categorized each video correctly (per video confidence) and to estimate how many videos they correctly categorized out of 20 (overall confidence). The sample performed above chance on the detection activity, correctly categorizing 60.70% of videos on average (SD = 13.00). The detection strategies intervention did not impact detection accuracy or detection confidence, with the intervention and control groups performing similarly on the detection activity and showing similar levels of confidence. Inconsistent with previous research, the study did not find that participants had a bias toward categorizing videos as real. Participants overestimated their ability to detect deepfakes at the individual video level. However, they tended to underestimate their abilities on the overall confidence question
Categorization · Confidence interval · Statistics · Aesthetic Perception and Analysis · Computer Science · Face Recognition and Perception · Psychology · Psychology of Moral and Emotional Judgment · Artificial Intelligence
Human Performance in Deepfake Detection
A (Mid)journey Through Reality
Looks real, feels fake
Detection and spill-over effects of AI-generated images in political messages
Conspiracy thinking and social media use are associated with ability to detect deepfakes
Identifying individual differences in deepfake discernment
Framing digital inauthenticity
Deepfake Technology and Gender-Based Violence
Mapping the landscape of deepfake research
Experiences with AI-Generated Pornography
| Unique citing works | 10 |
|---|---|
| Citations per year | 5 |
| Citation span | 2024 - 2026 (3) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 10 |