Hey all -- founder here,
We've built the best Deepfake detector and only detection API out there. It catches >96% of deepfakes.
In addition to the API, it can run locally on mobile devices using CoreML and Tensorflow Mobile.
Can you describe some of your methodology for detecting the fakes? Presumably you have trained a detector on a large data set; did you generate this yourself using several popular deepfake tools?
I can't go into depth on our models or data sets, because this is a cat and mouse game between creation/detection. We create deepfakes using all known tools, currently non-public methods as well as our own methods.