Authors:
Nils C. Köbis, Barbora Dolezalová, Ivan Soraperra
Where published:
iScience
Publication Date:
November 19, 2021
Dataset names (used for):
- Main experiment, video coding: 3,000 deepfake videos and their corresponding original short clips from Kaggle’s DeepFake Detection Challenge
The study investigates human detection abilities for deepfake videos, focusing on detection accuracy, cognitive biases, and overconfidence. Participants view 16 videos (8 authentic and 8 deepfakes) and provide responses on whether the video is a deepfake, along with confidence ratings and demographic information.
The dataset includes 16 videos per participant (8 authentic and 8 deepfakes), with responses on deepfake detection tasks, confidence ratings, and demographic information. The videos are sourced from MIT’s DetectDeepfake project and Kaggle’s DeepFake Detection Challenge.
Keywords:
Deepfakes, detection accuracy, cognitive biases, overconfidence, AI-manipulated media, fake videos
Instance Represent:
Responses from participants on deepfake detection tasks, including demographic details and confidence ratings
Dataset Characteristics:
Includes videos from the MIT DetectDeepfake project and Kaggle’s DeepFake Detection Challenge. Contains numerical data (confidence ratings) and categorical data (demographic details, video type)
Subject Area:
Behavioral economics, cognitive psychology, digital misinformation
Associated Tools:
Deepfake detection, confidence assessment, bias analysis
Feature Type:
Numerical (confidence ratings), Categorical (demographic details, video type)
License: © 2021 by the authors. This article is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives (CC BY-NC-ND) license.
Last Accessed: 7/7/2024