DeepFake-o-meter: An Open Platform for DeepFake Detection
Yuezun Li, Cong Zhang, Pu Sun, Lipeng Ke, Yan Ju, Honggang Qi, Siwei Lyu
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.
Topics & Concepts
Computer scienceMetreTrustworthinessOpen sourceWork (physics)Function (biology)Data scienceHuman–computer interactionArtificial intelligenceComputer securitySoftwareEngineeringOperating systemAstronomyPhysicsMechanical engineeringBiologyEvolutionary biologyAdversarial Robustness in Machine LearningFace recognition and analysisDigital Media Forensic Detection