| Title | DeepFake-o-meter: An Open Platform for DeepFake Detection |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Li, Yuezun, Zhang, Cong, Sun, Pu, Ke, Lipeng, Ju, Yan, Qi, Honggang, Lyu, Siwei |
| Conference Name | 2021 IEEE Security and Privacy Workshops (SPW) |
| Keywords | Conferences, DeepFake, deepfake detection, faces, human factors, Media, Metrics, multimedia forensics, Open Source Software, privacy, pubcrawl, resilience, Resiliency, Scalability, security, software engineering, 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. |
| DOI | 10.1109/SPW53761.2021.00047 |
| Citation Key | li_deepfake-o-meter_2021 |
DeepFake-o-meter: An Open Platform for DeepFake Detection