To Spot AI Fakes, a Little Training Goes a Long Way

Matthew Groh, an assistant professor of management and organizations at Kellogg School of Management, has been studying the ability to distinguish between real and artificially generated images. In his research, he found that even professionals who deal with this type of content on a daily basis are not necessarily better at identifying AI-generated images than regular people. This is because the latest AI models have improved significantly in recent years, making it easier for anyone to create realistic-looking pictures by feeding them text prompts.

In one study, Groh and his colleagues tested how well ordinary people could distinguish between real and fake images. They found that participants correctly classified about three-quarters of the time, with their accuracy improving as they studied each picture longer. While this is not perfect, it shows that most people are far from random guessing when trying to identify AI-generated images.

The stakes for accurately identifying these types of images can be high, especially in fields like national security. Intelligence analysts who fail to correctly screen out fake images may inadvertently authorize actions based on false information. For instance, officials might launch a drone strike without just cause if they believe an image suggests a terrorist is present at a particular location.

To address this issue, Groh and his colleagues developed a 30-minute training session that points out common patterns and tell-tale signs in real versus AI-generated images. They tested the effectiveness of this training with a group of 32 intelligence analysts from various U.S. agencies who write daily briefs for the president and other senior White House officials.

The training session included a customized web interface where participants could classify 40 images as either real or AI-generated, along with writing down their reasons for each decision. The collection of images was diverse, including portraits, full-body shots, posed group shots, and candids, all paired based on content. For example, the image set might include both a real photograph of a female astronaut and an AI-generated one.

Before receiving the training, the analysts’ accuracy in identifying real versus fake images was about the same as that of regular people - around 73%. However, after completing the training session, their accuracy increased to 82%, with a significant bump of 9 percentage points. The participants also showed improved ability to accurately classify both images in a pair.

The improvement was largely driven by an increase in identifying real images correctly, which is actually more challenging than spotting AI-generated artifacts. As Groh notes, ‘a real image has the absence of artifacts.’ Without this skill, people may be inclined to believe that all images are fake by default, leading to potential problems.

One common giveaway for AI-generated images is their tendency to look overly perfect and cinematographic, featuring individuals with symmetrical, classically beautiful features. Other signs include waxy or glossy skin, missing teeth, or implausible scenarios such as a doctor carrying a stethoscope with the two earpieces merged into a loop.

The analysts’ ability to identify real versus fake images improved not only in terms of accuracy but also in their reasoning behind each decision. Before receiving the training, they tended to provide vague comments, often naming body parts or stating that something ‘looks off.’ Afterward, they were more likely to pinpoint specific details reflecting key patterns.

Groh emphasizes the importance of developing this skill for professionals who deal with AI-generated images on a daily basis. He notes that policymakers should also establish guardrails around AI image generation, such as requirements for when and how creators must disclose that their images are fake. However, until these regulations come into place, people need to build this skill themselves.

One way to improve accuracy is through prepared trainings like the one developed by Groh and his colleagues. Another approach is experimenting with AI models on your own, which can help you notice the signs that tend to show up in AI-generated images. As Groh says, ‘the biggest thing is playing around with these tools themselves.’ By doing so, people will start to see their limitations.

Groh’s research highlights the need for professionals and individuals alike to develop a basic understanding of how AI image generation works and what signs indicate an image may be fake. This skill is critical not only in fields like national security but also in business settings where trust is fundamental. As Groh notes, ‘the moment that you can’t tell the difference at all is the moment you can no longer trust any visual medium.’