Abstract
Recent advancements in machine learning and computer vision have led to the proliferation of Deepfakes. As technology democratizes over time, there is an increasing fear that novice users can create Deepfakes, to discredit others and undermine public discourse. In this paper, we conduct user studies to understand whether participants with advanced computer skills and varying level of computer science expertise can create Deepfakes of a person saying a target statement using limited media files. We conduct two studies; in the first study (n = 39) participants try creating a target Deepfake in a constrained time frame using any tool they desire. In the second study (n = 29) participants use pre-specified deep learning based tools to create the same Deepfake. We find that for the first study, of the participants successfully created complete Deepfakes with audio and video, whereas for the second user study, of the participants were successful in stitching target speech to the target video. We further use Deepfake detection software tools as well as human examiner-based analysis, to classify the successfully generated Deepfake outputs as fake, suspicious, or real. The software detector classified of the Deepfakes as fake, whereas the human examiners classified of the videos as fake. We conclude that creating Deepfakes is a simple enough task for a novice user given adequate tools and time; however, the resulting Deepfakes are not sufficiently real-looking and are unable to completely fool detection software as well as human examiners.
Original language | English |
---|---|
Title of host publication | Companion Proceedings of the ACM Web Conference 2023 (WWW ’23 Companion), April 30–May 04, 2023, Austin, TX, USA |
Editors | Ying Ding, Jie Tang, Juan Sequeda, Lora Aroyo, Carlos Castillo |
Place of Publication | New York |
Publisher | ACM - Association for Computing Machinery |
Pages | 1324-1334 |
Number of pages | 11 |
ISBN (Electronic) | 978-145039416-1 |
DOIs | |
Publication status | Published - 30 Apr 2023 |
Event | 2023 World Wide Web Conference, WWW 2023 - Austin, United States Duration: 30 Apr 2023 → 4 May 2023 |
Conference
Conference | 2023 World Wide Web Conference, WWW 2023 |
---|---|
Country/Territory | United States |
City | Austin |
Period | 30/04/23 → 4/05/23 |
Keywords
- deepfakes
- generative models
- video synthesis