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Filters: Author is Sun, Pu  [Clear All Filters]
2022-04-25
Li, Yuezun, Zhang, Cong, Sun, Pu, Ke, Lipeng, Ju, Yan, Qi, Honggang, Lyu, Siwei.  2021.  DeepFake-o-meter: An Open Platform for DeepFake Detection. 2021 IEEE Security and Privacy Workshops (SPW). :277–281.
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.