For weeks, computer scientist Siwei Lyu had watched his team’s deepfake videos with a gnawing sense of unease. Created by a machine learning algorithm, these falsified films showed celebrities doing things they'd never done. They felt eerie to him, and not just because he knew they’d been ginned up. “They don’t look right,” he recalls thinking, “but it’s very hard to pinpoint where that feeling comes from.”

Finally, one day, a childhood memory bubbled up into his brain. He, like many kids, had held staring contests with his open-eyed peers. “I always lost those games,” he says, “because when I watch their faces and they don’t blink, it makes me very uncomfortable.”

These lab-spun deepfakes, he realized, were needling him with the same discomfort: He was losing the staring contest with these film stars, who didn't open and close their eyes at the rates typical of actual humans.

To find out why, Lyu, a professor at the University of Albany, and his team dug into every step in the software, called DeepFake, that had created them.

Deepfake programs pull in lots of images of a particular person—you, your ex-girlfriend, Kim Jong-un—to catch them at different angles, with different expressions, saying different words. The algorithms learn what this character looks like, and then synthesize that knowledge into a video showing that person doing something he or she never did. Make porn. Make Stephen Colbert spout words actually uttered by John Oliver. Provide a presidential meta-warning about fake videos.

These fakes, while convincing if you watch a few seconds on a phone screen, aren’t perfect (yet). They contain tells, like creepily ever-open eyes, from flaws in their creation process. In looking into DeepFake’s guts, Lyu realized that the images that the program learned from didn’t include many with closed eyes (after all, you wouldn’t keep a selfie where you were blinking, would you?). “This becomes a bias,” he says. The neural network doesn’t get blinking. Programs also might miss other “physiological signals intrinsic to human beings,” says Lyu’s paper on the phenomenon, such as breathing at a normal rate, or having a pulse. (Autonomic signs of constant existential distress are not listed.) While this research focused specifically on videos created with this particular software, it is a truth universally acknowledged that even a large set of snapshots might not adequately capture the physical human experience, and so any software trained on those images may be found lacking.

Lyu's blinking revelation revealed a lot of fakes. But a few weeks after his team put a draft of their paper online, they got anonymous emails with links to deeply faked YouTube videos whose stars opened and closed their eyes more normally. The fake content creators had evolved.

Of course they had. As Lyu noted in a piece for The Conversation, “blinking can be added to deepfake videos by including face images with closed eyes or using video sequences for training.” Once you know what your tell is, avoiding it is "just" a technological problem. Which means deepfakes will likely become (or stay) an arms race between the creators and the detectors. But research like Lyu’s can at least make life harder for the fake-makers. “We are trying to raise the bar,” he says. “We want to make the process more difficult, more time-consuming.”

Because right now? It's pretty easy. You download the software. You Google “Hillary Clinton.” You get tens of thousands of images. You funnel them into the deepfake pipeline. It metabolizes them, learns from them. And while it's not totally self-sufficient, with a little help, it gestates and gives birth to something new, something sufficiently real.