iFakeDetector: Real Time Integrated Web-based Deepfake Detection System
Kangjun Lee, Inho Jung, Simon S. Woo
Abstract
Recently, deepfake detection research has been actively conducted. While many deepfake detectors have been proposed, validating the practicality of such systems against real world settings has not been explored much. Indeed, there are some gaps and disparities when they are applied in the real world. In this work, we developed a real time integrated web-based deepfake detection system, iFakeDetector, which incorporates the recent high performing deepfake detectors, and enables easy access for non-expert users to evaluate deepfake videos. Our system takes a deepfake video as input, allowing users to upload videos and select different detectors, and provides detection results on whether the uploaded video is a deepfake or not. Also, we provide an analysis tool that enables the video to be analyzed on a frame-by-frame basis with the probability of each frame being manipulated. Finally, we tested and deployed iFakeDetector in a real world scenario to verify its practicality and feasibility.
BibTeX
@inproceedings{ijcai2024p1016,
title = {iFakeDetector: Real Time Integrated Web-based Deepfake Detection System},
author = {Lee, Kangjun and Jung, Inho and Woo, Simon S.},
booktitle = {Proceedings of the Thirty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-24}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Kate Larson},
pages = {8717--8720},
year = {2024},
month = {8},
note = {Demo Track},
doi = {10.24963/ijcai.2024/1016},
url = {https://doi.org/10.24963/ijcai.2024/1016},
}