In 2011, Hany Farid, a photo-forensics expert, received an e-mail from a bereaved father. Three years earlier, the man’s son had found himself on the side of the road with a car that wouldn’t start. When some strangers offered him a lift, he accepted. A few minutes later, for unknown reasons, they shot him. A surveillance camera had captured him as he walked toward their car, but the video was of such low quality that key details, such as faces, were impossible to make out. The other car’s license plate was visible only as an indecipherable jumble of pixels. The father could see the evidence that pointed to his son’s killers—just not clearly enough.

Farid had pioneered the forensic analysis of digital photographs in the late nineteen-nineties, and gained a reputation as a miracle worker. As an expert witness in countless civil and criminal trials, he explained why a disputed digital image or video had to be real or fake. Now, in his lab at Dartmouth, where he was a professor of computer science, he played the father’s video over and over, wondering if there was anything he could do. On television, detectives often “enhance” photographs, sharpening the pixelated face of a suspect into a detailed portrait. In real life, this is impossible. As the video had flowed through the surveillance camera’s “imaging pipeline”—the lens, the sensor, the compression algorithms—its data had been “downsampled,” and, in the end, very little information remained. Farid told the father that the degradation of the image couldn’t be reversed, and the case languished, unsolved.

A few months later, though, Farid had a thought. What if he could use the same surveillance camera to photograph many, many license plates? In that case, patterns might emerge—correspondences between the jumbled pixels and the plates from which they derived. The correspondences would be incredibly subtle: the particular blur of any degraded image would depend not just on the plate numbers but also on the light conditions, the design of the plate, and many other variables. Still, if he had access to enough images—hundreds of thousands, perhaps millions—patterns might emerge.

Such an undertaking seemed impractical, and for a while it was. But a new field, “image synthesis,” was coming into focus, in which computer graphics and A.I. were combined. Progress was accelerating. Researchers were discovering new ways to use neural networks—software systems based, loosely, on the architecture of the brain—to analyze and create images and videos. In the emerging world of “synthetic media,” the work of digital-image creation—once the domain of highly skilled programmers and Hollywood special-effects artists—could be automated by expert systems capable of producing realism on a vast scale.

In a media environment saturated with fake news, such technology has disturbing implications. Last fall, an anonymous Redditor with the username Deepfakes released a software tool kit that allows anyone to make synthetic videos in which a neural network substitutes one person’s face for another’s, while keeping their expressions consistent. Along with the kit, the user posted pornographic videos, now known as “deepfakes,” that appear to feature various Hollywood actresses. (The software is complex but comprehensible: “Let’s say for example we’re perving on some innocent girl named Jessica,” one tutorial reads. “The folders you create would be: ‘jessica; jessica_faces; porn; porn_faces; model; output.’ ”) Around the same time, “Synthesizing Obama,” a paper published by a research group at the University of Washington, showed that a neural network could create believable videos in which the former President appeared to be saying words that were really spoken by someone else. In a video voiced by Jordan Peele, Obama seems to say that “President Trump is a total and complete dipshit,” and warns that “how we move forward in the age of information” will determine “whether we become some kind of fucked-up dystopia.”

Not all synthetic media is dystopian. Recent top-grossing movies (“Black Panther,” “Jurassic World”) are saturated with synthesized images that, not long ago, would have been dramatically harder to produce; audiences were delighted by “Star Wars: The Last Jedi” and “Blade Runner 2049,” which featured synthetic versions of Carrie Fisher and Sean Young, respectively. Today’s smartphones digitally manipulate even ordinary snapshots, often using neural networks: the iPhone’s “portrait mode” simulates what a photograph would have looked like if it been taken by a more expensive camera. Meanwhile, for researchers in computer vision, A.I., robotics, and other fields, image synthesis makes whole new avenues of investigation accessible.

Farid started by sending his graduate students out on the Dartmouth campus to photograph a few hundred license plates. Then, based on those photographs, he and his team built a “generative model” capable of synthesizing more. In the course of a few weeks, they produced tens of millions of realistic license-plate images, each one unique. Then, by feeding their synthetic license plates through a simulated surveillance camera, they rendered them indecipherable. The aim was to create a Rosetta Stone, connecting pixels to plate numbers.

Next, they began “training” a neural network to interpret those degraded images. Modern neural networks are multilayered, and each layer juggles millions of variables; tracking the flow of information through such a system is like following drops of water through a waterfall. Researchers, unsure of how their creations work, must train them by trial and error. It took Farid’s team several attempts to perfect theirs. Eventually, though, they presented it with a still from the video. “The license plate was like ten pixels of noise,” Farid said. “But there was still a signal there.” Their network was “pretty confident about the last three characters.”

This summer, Farid e-mailed those characters to the detective working the case. Investigators had narrowed their search to a subset of blue Chevy Impalas; the network pinpointed which one. Someone connected to the car turned out to have been involved in another crime. A case that had lain dormant for nearly a decade is now moving again. Farid and his team, meanwhile, published their results in a computer-vision journal. In their paper, they noted that their system was a free upgrade for millions of low-quality surveillance cameras already in use. It was a paradoxical outcome typical of the world of image synthesis, in which unreal images, if they are realistic enough, can lead to the truth.

Farid is in the process of moving from Dartmouth to the University of California, Berkeley, where his wife, the psychologist Emily Cooper, studies human vision and virtual reality. Their modernist house, perched in the hills above the Berkeley campus, is enclosed almost entirely in glass; on a clear day this fall, I could see through the living room to the Golden Gate Bridge. At fifty-two, Farid is gray-haired, energized, and fit. He invited me to join him on the deck. “People have been doing synthesis for a long time, with different tools,” he said. He rattled off various milestones in the history of image manipulation: the transposition, in a famous photograph from the eighteen-sixties, of Abraham Lincoln’s head onto the body of the slavery advocate John C. Calhoun; the mass alteration of photographs in Stalin’s Russia, designed to purge his enemies from the history books; the convenient realignment of the pyramids on the cover of National Geographic, in 1982; the composite photograph of John Kerry and Jane Fonda standing together at an anti-Vietnam demonstration, which incensed many voters after the Times credulously reprinted it, in 2004, above a story about Kerry’s antiwar activities.