iFakeDetector: Real Time Integrated Web-based Deepfake Detection System
iFakeDetector: Real Time Integrated Web-based Deepfake Detection System
Kangjun Lee, Inho Jung, Simon S. Woo
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence
Demo Track. Pages 8717-8720.
https://doi.org/10.24963/ijcai.2024/1016
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.
Keywords:
AI Ethics, Trust, Fairness: ETF: Ethical, legal and societal issues
AI Ethics, Trust, Fairness: ETF: Societal impact of AI
Computer Vision: CV: Applications
Computer Vision: CV: Machine learning for vision
Humans and AI: HAI: Applications
Humans and AI: HAI: Human-computer interaction