A Representative Study on Human Detection of Artificially Generated Media Across Countries

authored by: Joel Frank, Franziska Herbert, Jonas Ricker, Lea Schönherr, Thorsten Eisenhofer, Asja Fischer, Markus Dürmuth, Thorsten Holz
Abstract: AI-generated media has become a threat to our digital society as we know it. Forgeries can be created automatically and on a large scale based on publicly available technologies. Recognizing this challenge, academics and practitioners have proposed a multitude of automatic detection strategies to detect such artificial media. However, in contrast to these technological advances, the human perception of generated media has not been thoroughly studied yet.In this paper, we aim to close this research gap. We conduct the first comprehensive survey on people's ability to detect generated media, spanning three countries (USA, Germany, and China), with 3,002 participants covering audio, image, and text media. Our results indicate that state-of-the-art forgeries are almost indistinguishable from "real"media, with the majority of participants simply guessing when asked to rate them as human- or machine-generated. In addition, AI-generated media is rated as more likely to be human-generated across all media types and all countries. To further understand which factors influence people's ability to detect AI-generated media, we include personal variables, chosen based on a literature review in the domains of deepfake and fake news research. In a regression analysis, we found that generalized trust, cognitive reflection, and self-reported familiarity with deepfakes significantly influence participants' decisions across all media categories.
Organisation(s): Usable Security and Privacy Section
External Organisation(s): Ruhr-Universität Bochum
CISPA Helmholtz Center for Information Security
Technische Universität Berlin
Type: Conference contribution
Pages: 55-73
No. of pages: 19
Publication date: 23.05.2024
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Software, Safety, Risk, Reliability and Quality, Computer Networks and Communications
Electronic version(s): https://doi.org/10.48550/arXiv.2312.05976 (Access: Open)
https://doi.org/10.1109/SP54263.2024.00159 (Access: Closed)

BibTeX

@inproceedings{edf9270049f04fddad5e554b359ad454,
title = "A Representative Study on Human Detection of Artificially Generated Media Across Countries",
abstract = "AI-generated media has become a threat to our digital society as we know it. Forgeries can be created automatically and on a large scale based on publicly available technologies. Recognizing this challenge, academics and practitioners have proposed a multitude of automatic detection strategies to detect such artificial media. However, in contrast to these technological advances, the human perception of generated media has not been thoroughly studied yet.In this paper, we aim to close this research gap. We conduct the first comprehensive survey on people's ability to detect generated media, spanning three countries (USA, Germany, and China), with 3,002 participants covering audio, image, and text media. Our results indicate that state-of-the-art forgeries are almost indistinguishable from {"}real{"}media, with the majority of participants simply guessing when asked to rate them as human- or machine-generated. In addition, AI-generated media is rated as more likely to be human-generated across all media types and all countries. To further understand which factors influence people's ability to detect AI-generated media, we include personal variables, chosen based on a literature review in the domains of deepfake and fake news research. In a regression analysis, we found that generalized trust, cognitive reflection, and self-reported familiarity with deepfakes significantly influence participants' decisions across all media categories.",
author = "Joel Frank and Franziska Herbert and Jonas Ricker and Lea Sch{\"o}nherr and Thorsten Eisenhofer and Asja Fischer and Markus D{\"u}rmuth and Thorsten Holz",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.",
year = "2024",
month = may,
day = "23",
doi = "10.48550/arXiv.2312.05976",
language = "English",
series = "Proceedings - IEEE Symposium on Security and Privacy",
pages = "55--73",
booktitle = "Proceedings 45th IEEE Symposium on Security and Privacy",
}