How to Tell If a Photo Is an AI-Generated Fake

1. You may have seen photographs that suggest otherwise, but former

president Donald Trump wasn’t arrested last week, and the pope didn’t wear a

stylish, brilliant white puffer coat. These recent viral hits were the fruits of

artificial intelligence systems that process a user’s textual prompt to create

images. They demonstrate how these programs have become very good very

quickly—and are now convincing enough to fool an unwitting observer.

2. So how can skeptical viewers spot images that may have been generated by

an artificial intelligence system such as DALL-E, Midjourney or Stable

Diffusion? Each AI image generator—and each image from any given

generator—varies in how convincing it may be and in what telltale signs might

give its algorithm away. For instance, AI systems have historically struggled to

mimic human hands and have produced mangled appendages with too many

digits. As the technology improves, however, systems such as Midjourney V5

seem to have cracked the problem—at least in some examples. Across the board,

experts say that the best images from the best generators are difficult, if not

impossible, to distinguish from real images.

3. “It’s pretty amazing, in terms of what AI image generators are able to do,”

says S. Shyam Sundar, a researcher at Pennsylvania State University who

studies the psychological impacts of media technologies. “There’s been a giant

leap in the last year or so in terms of image-generation abilities.”

4. Some of the factors behind this leap in ability include the ever-increasing

number of images available to train such AI systems, as well as advances in data

processing infrastructure and interfaces that make the technology accessible to

regular Internet users, Sundar notes. The result is that artificially generated

images are everywhere and can be “next to impossible to detect,” he says.

5. One recent experiment highlighted how well AI is able to deceive. Sophie

Nightingale, a psychologist at Lancaster University in England who focuses on

digital technology, co-authored a study that tested whether online volunteers

could distinguish between passportlike headshots created by an AI system

called StyleGAN2 and real images. The results were disheartening, even back in

late 2021, when the researchers ran the experiment. “On average, people were

pretty much at chance performance,” Nightingale says. “Basically, we’re at the

point where it’s so realistic that people can’t reliably perceive the difference

between those synthetic faces and actual, real faces—faces of actual people who

really exist.” Although humans provided some help to the AI (researchers sorted

through the images generated by StyleGAN2 to select only the most realistic

ones), Nightingale says that someone looking to use such a program for

nefarious purposes would likely do the same.

6. In a second test, the researchers tried to help the test subjects improve their

AI-detecting abilities. They marked each answer right or wrong after

participants answered, and they also prepared participants in advance by having

them read through advice for detecting artificially generated images. That

advice highlighted areas where AI algorithms often stumble and create

mismatched earrings, for example, or blur a person’s teeth together. Nightingale

also notes that algorithms often struggle to create anything more sophisticated

than a plain background. But even with these additions, participants’ accuracy

only increased by about 10 percent, she says—and the AI system that generated

the images used in the trial has since been upgraded to a new and improved

version.

7. Ironically, as image-generating technology continues to improve, humans’

best defense from being fooled by an AI system may be yet another AI system:

one trained to detect artificial images. Experts say that as AI image generation

progresses, algorithms are better equipped than humans to detect some of the

tiny, pixel-scale fingerprints of robotic creation.

8. Creating these AI detective programs works the same way as any other

machine learning task, says Yong Jae Lee, a computer scientist at the University

of Wisconsin–Madison. “You collect a data set of real images, and you also

collect a data set of AI-generated images,” Lee says. “Then you can train a

machine-learning model to distinguish the two.”

9. Still, these systems have significant shortcomings, Lee and other experts

say. Most such algorithms are trained on images from a specific AI generator

and are unable to identify fakes produced by different algorithms. (Lee says he

and a research team are working on a way to avoid that problem by training the

detector to instead recognize what makes an image real.) Most detectors also

lack the user-friendly interfaces that have tempted so many people to try the

generative AI systems.

10. Moreover AI detectors will always be scrambling to keep up with AI image

generators, some of which incorporate similar detection algorithms but use

them as a way to learn how to make their fake output less detectable. “The battle

between AI systems that generate images and AI systems that detect the AI-

generated images is going to be an arms race,” says Wael AbdAlmageed, a

research associate professor of computer science at the University of Southern

California. “I don’t see any side winning anytime soon.”

11. AbdAlmageed says no approach will ever be able to catch every single

artificially produced image—but that doesn’t mean we should give up. He

suggests that social media platforms need to begin confronting AI-generated

content on their sites because these companies are better posed to implement

detection algorithms than individual users are.

12. And users need to more skeptically evaluate visual information by asking

whether it’s false, AI-generated or harmful before sharing. “We as human

species sort of grow up thinking that seeing is believing,” AbdAlmageed says.

“That’s not true anymore. Seeing is not believing anymore.”

Understanding

1. What are some factors behind this giant leap in the last year or so in terms

of image generation abilities?

2. In the second test, what did the researchers do to help the test subjects

improve their AI-detecting abilities? And by about what percent participants’

accuracy increased?

3. In paragraph 10, Wael AbdAlmageed said that “I don’t see any side winning

anytime soon.” What are those two sides?

Discussion

1. Have you ever been deceived by images that were generated by an AI?

2. What are the pros and cons of AI image generators?

3. In paragraph 11, AbdAlmageed suggests that social media platforms need to

begin confronting AI-generated content on their sites. Do you agree with him?

4. Have you ever realized that seeing is believing? OR Have you ever realized

that seeing is not believing anymore?