@inbook{51eb5171444e42bfba40080bb2b9ce10,
title = "FakeBuster: A deepfakes detection tool for video conferencing scenarios",
abstract = "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.",
keywords = "Deepfakes detection, Neural networks, Spoofing",
author = "Vineet Mehta and Parul Gupta and Ramanathan Subramanian and Abhinav Dhall",
note = "Publisher Copyright: {\textcopyright} 2021 Owner/Author. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.; 26th International Conference on Intelligent User Interfaces: Where HCI Meets AI, IUI 2021 ; Conference date: 14-04-2021 Through 17-04-2021",
year = "2021",
month = apr,
day = "14",
doi = "10.1145/3397482.3450726",
language = "English",
series = "International Conference on Intelligent User Interfaces, Proceedings IUI",
publisher = "Association for Computing Machinery (ACM)",
pages = "61--63",
editor = "Katrien Verbert and Dennis Parra",
booktitle = "26th International Conference on Intelligent User Interfaces, IUI 2021 Companion",
address = "United States",
}