Can Deepfakes be created on a whim?

Pulak Mehta, Gauri Jagatap, Kevin Gallagher, Brian Timmerman, Progga Deb, Siddharth Garg, Rachel Greenstadt, Brendon Dolan-Gavitt

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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 languageEnglish
Title of host publicationCompanion Proceedings of the ACM Web Conference 2023 (WWW ’23 Companion), April 30–May 04, 2023, Austin, TX, USA
EditorsYing Ding, Jie Tang, Juan Sequeda, Lora Aroyo, Carlos Castillo
Place of PublicationNew York
PublisherACM - Association for Computing Machinery
Pages1324-1334
Number of pages11
ISBN (Electronic)978-145039416-1
DOIs
Publication statusPublished - 30 Apr 2023
Event2023 World Wide Web Conference, WWW 2023 - Austin, United States
Duration: 30 Apr 20234 May 2023

Conference

Conference2023 World Wide Web Conference, WWW 2023
Country/TerritoryUnited States
CityAustin
Period30/04/234/05/23

Keywords

  • deepfakes
  • generative models
  • video synthesis

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