Myth 1: Deep fakes predominantly take the form of face swapping in videos
Reality: There are four main types of deep fake: face replacement (aka face swapping), face re-enactment (aka puppetry), face generation, and audio synthesis. Myth 2: Deepfakes are cropping up in large numbers on social media platforms
Reality: Few political deepfakes have emerged on social media platforms. However, the number of deepfake pornographic videos is a cause for concern. Myth 3: Anyone can make sophisticated deepfakes that pass the bar of believability
Reality: While supportive software like FakeApp has allowed more people to try their hand at deepfakes, high quality audio and visual synthesis still requires considerable expertise. Myth 4: The best way of detecting deepfakes is through physiological testing (e.g. the ‘eye blinking’ test)
Reality: Physiological examination of videos can be slow and unreliable. Systematic screening of deepfakes will require AI-based tools that can partially automate the detection of forged content. Tools will also need to be regularly updated. Myth 5: New legislation is a quick solution for dealing with deepfakes
Reality: Attempts to legislate against deepfakes may prove ineffective, given it is very difficult to identify where doctored content originates. Legislation could also threaten beneficial uses of visual and audio manipulation. Myth 6: Deepfakes are just like photoshopped images. People will get used to them
Reality: This is an assumption, not a fact. There has been insufficient research on how deepfakes influence the behaviour and beliefs of viewers.