FakeBuster: a deepfakes detection tool for video conferencing scenarios

Vineet Mehta, Parul Gupta, Ramanathan Subramanian, Abhinav Dhall

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

13 Citations (Scopus)


This paper proposes FakeBuster, a novel DeepFake detector for (a) detecting impostors during video conferencing, and (b) manipulated faces on social media. FakeBuster is a standalone deep learning- based solution, which enables a user to detect if another person's video is manipulated or spoofed during a video conference-based meeting. This tool is independent of video conferencing solutions and has been tested with Zoom and Skype applications. It employs a 3D convolutional neural network for predicting video fakeness. The network is trained on a combination of datasets such as Deeperforensics, DFDC, VoxCeleb, and deepfake videos created using locally captured images (specific to video conferencing scenarios). Diversity in the training data makes FakeBuster robust to multiple environments and facial manipulations, thereby making it generalizable and ecologically valid.

Original languageEnglish
Title of host publication26th International Conference on Intelligent User Interfaces, IUI 2021 Companion
EditorsBart Knijnenburg, John O’Donovan, Paul Teale
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Number of pages3
ISBN (Electronic)9781450380188
Publication statusPublished - Apr 2021
EventInternational Conference on Intelligent User Interfaces 2021: Where HCI Meets AI - Online, United States of America
Duration: 14 Apr 202117 Apr 2021
Conference number: 26th
https://dl.acm.org/doi/proceedings/10.1145/3397482 (Proceedings)


ConferenceInternational Conference on Intelligent User Interfaces 2021
Abbreviated titleIUI 2021
Country/TerritoryUnited States of America
Internet address


  • Deepfakes detection
  • Neural networks
  • Spoofing

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