Facebook says the winning algorithm in the contest was able to spot “challenging real world examples” of deepfakes with an average accuracy of 65.18 percent. That’s not bad, but it’s not the sort of hit-rate you would want for any automated system.
Deepfakes have proven to be something of an exaggerated menace for social media. Although the technology prompted much handwringing about the erosion of reliable video evidence, the political effects of deepfakes have so far been minimal. Instead, the more immediate harm has been the creation of nonconsensual pornography, a category of content that’s easier for social media platforms to identify and remove.
Mike Schroepfer, Facebook’s chief technology officer, told journalists in a press call that he was pleased by the results of the challenge, which he said would create a benchmark for researchers and guide their work in the future. “Honestly the contest has been more of a success than I could have ever hoped for,” he said.
Some 2,114 participants submitted more than 35,000 detection algorithms to the competition. They were tested on their ability to identify deepfake videos from a dataset of around 100,000 short clips. Facebook hired more than 3,000 actors to create these clips, who were recorded holding conversations in naturalistic environments. Some clips were altered using AI by having other actors’ faces pasted on to their videos.
Researchers were given access to this data to train their algorithms, and when tested on this material, they produced accuracy rates as high as 82.56 percent. However, when the same algorithms were tested against a “black box” dataset consisting of unseen footage, they performed much worse, with the best-scoring model achieving an accuracy rate of 65.18 percent. This shows detecting deepfakes in the wild is a very challenging problem.
