The Technology That Will Make It Impossible for You to Believe What You See (VIDEO)
With these techniques, it’s difficult to discern between videos of real people and computerized impostors that can be programmed to say anything.
July 11, 2017
Reprinted from: The Atlantic
At a time when distrust in journalistic institutions is swelling, technology that further muddies the ability to discern what’s real is rapidly advancing. Convincing Photoshop-esque techniques for video have arrived, and the result is simultaneously terrifying and remarkable.
Computer scientists can now make realistic lip-synched videos—ostensibly putting anyone’s words into another person’s mouth.
The animated gif that you see above? That’s not actually Barack Obama speaking. It’s a synthesized video of Obama, made to appear as though he’s speaking words that were actually inputted from an audio file.
Obama was a natural subject for this kind of experiment because there are so many readily available, high-quality video clips of him speaking. In order to make a photo-realistic mouth texture, researchers had to input many, many examples of Obama speaking—layering that data atop a more basic mouth shape. The researchers used what’s called a recurrent neural network to synthesize the mouth shape from the audio. (This kind of system, modeled on the human brain, can take in huge piles of data and find patterns. Recurrent neural networks are also used for facial recognition and speech recognition.) They trained their system using millions of existing video frames. Finally, they smoothed out the footage using compositing techniques applied to real footage of Obama’s head and torso.
There are ways for experts to determine whether a video has been faked using this technique. Since researchers still rely on legitimate footage to produce portions of a lip-synched video, like the speaker’s head, it’s possible to identify the original video that was used to create the made-up one.
“So, by creating a database of internet videos, we can detect fake videos by searching through the database and see whether there exists a video with the same head and background,” Suwajanakorn told me. “Another artifact that can be an indication is the blurry mouth [and] teeth region. This may be not noticeable by human eyes, but a program that compares the blurriness of the mouth region to the rest of the video can easily be developed and will work quite reliably.”
It also helps if you have two or more recordings of a person from different views, Suwajanakorn said. That’s much harder to fake. These are useful safeguards, but the technology will still pose challenges as people realize its potential. Not everyone will know how to seek out the databases and programs that allow for careful vetting—or even think to question a realistic-looking video in the first place. And those who share misinformation unintentionally will likely exacerbate the increasing distrust in experts who can help make sense of things
“My thought is that people will not believe videos, just like how we do not believe photos once we’re aware that tools like Photoshop exist,” Suwajanakorn told me. “This could be both good and bad, and we have to move on to a more reliable source of evidence.”
But what does reliability mean when you cannot believe your own eyes? With enough convincing distortions to reality, it becomes very difficult to know what’s real.