"Simply put, GANs pit two competing neural networks against each other in a fist-fight to the death," Hurd says. In the case of Bold Glamour, it's a competition between the camera's view of your face and the style TikTok wants to morph you into. "Because it uses you, it then compares aspects of your face to a dataset of images that start to match against your cheeks, eyes, eyebrows, lips, and more." Eventually, the technology combines the two sets of images into one. "If we do this fast enough, we can achieve a video framerate," Hurd says.

In a world of ubiquitous smart-phone cameras, drones, and Google Street View cars, there's probably never been a more important time to start protecting the identities of people unwittingly captured in photos and videos. But while websites like YouTube have started offering tools to obscure faces and other objects appearing in digital media, researchers have found that those protections can be defeated at an alarming rate thanks to recent advances in artificial intelligence. In a paper released earlier this month, researchers at UT Austin and Cornell University demonstrate that faces and objects obscured by blurring, pixelation, and a recently-proposed privacy system called P3 can be successfully identified by a neural network trained on image datasets--in some cases at a more consistent rate than humans. "We argue that humans may no longer be the'gold standard' for extracting information from visual data," the researchers write. "Recent advances in machine learning based on artificial neural networks have led to dramatic improvements in the state of the art for automated image recognition. Trained machine learning models now outperform humans on tasks such as object recognition and determining the geographic location of an image."