DeepFake-o-meter: An Open Platform for DeepFake Detection
Yuezun Li, Cong Zhang, Pu Sun, Honggang Qi, and Siwei Lyu
TL;DR
DeepFake-o-meter is an open-source platform that integrates advanced DeepFake detection methods to help users identify manipulated videos, addressing the growing threat of DeepFakes to online media trustworthiness.
Contribution
It introduces a publicly accessible platform combining multiple state-of-the-art DeepFake detection techniques for practical use.
Findings
Provides a user-friendly interface for DeepFake detection
Integrates multiple detection algorithms into one platform
Enhances accessibility and usability of DeepFake detection tools
Abstract
In recent years, the advent of deep learning-based techniques and the significant reduction in the cost of computation resulted in the feasibility of creating realistic videos of human faces, commonly known as DeepFakes. The availability of open-source tools to create DeepFakes poses as a threat to the trustworthiness of the online media. In this work, we develop an open-source online platform, known as DeepFake-o-meter, that integrates state-of-the-art DeepFake detection methods and provide a convenient interface for the users. We describe the design and function of DeepFake-o-meter in this work.
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Taxonomy
TopicsFace recognition and analysis · Adversarial Robustness in Machine Learning · Digital Media Forensic Detection