How to spot a deepfake: the maker of a detection tool shares the key giveaways
There are detection tools, made both commercially and in research labs, that can help. To use these deepfake detectors, you upload or link a piece of media that you suspect could be fake, and the detector will give a percent likelihood that it was AI-generated.
But your senses and an understanding of some key giveaways provide a lot of insight when analyzing media to see whether it’s a deepfake.
While regulations for deepfakes, particularly in elections, lag the quick pace of AI advancements, we have to find ways to figure out whether an image, audio or video is actually real.
Siwei Lyu made one of them, the DeepFake-o-meter, at the University of Buffalo. His tool is free and open-source, compiling more than a dozen algorithms from other research labs in one place. Users can upload a piece of media and run it through these different labs’ tools to get a sense of whether it could be AI-generated.
The DeepFake-o-meter shows both the benefits and limitations of AI-detection tools. When we ran a few known deepfakes through the various algorithms, the detectors gave a rating for the same video, photo or audio recording ranging from 0% to 100% likelihood of being AI-generated.
