But while we’ve learned to distrust user names and text more generally, pictures are different. You can’t synthesize a picture out of nothing, we assume; a picture had to be of someone. Sure a scammer could appropriate someone else’s picture, but doing so is a risky strategy in a world with google reverse search and so forth. So we tend to trust pictures. A business profile with a picture obviously belongs to someone. A match on a dating site may turn out to be 10 pounds heavier or 10 years older than when a picture was taken, but if there’s a picture, the person obviously exists.
No longer. New adverserial machine learning algorithms allow people to rapidly generate synthetic ‘photographs’ of people who have never existed. Already faces of this sort are being used in espionage.
Computers are good, but your visual processing systems are even better. If you know what to look for, you can spot these fakes at a single glance — at least for the time being. The hardware and software used to generate them will continue to improve, and it may be only a few years until humans fall behind in the arms race between forgery and detection.
Our aim is to make you aware of the ease with which digital identities can be faked, and to help you spot these fakes at a single glance.
I was at a conference last month where research was presented which concluded pretty solidly that the mechanisms used to make “deepfakes” meant that it was probably impossible to create artificial intelligence that can learn to distinguish between real and fake pictures of humans. Simply put, this is because the way we make such images is with generative adversarial networks, an AI technique which thrives upon having an effective discriminator component, and any research into differentiating between real and fake images feeds the capability of the next generation of discriminators!
Instead, then, the best medium-term defence against deepfakes is training humans to be able to identify them, and that’s what this website aims to do. I was pleased that I did very well on my first attempt (I sort-of knew what to look for already, based on a basic understanding of the underlying technologies) but I was also pleased that I was able to learn to do better with the aid of the authors’ tips. Nice.