Researchers find training humans to spot deepfakes boosts accuracy from 40% to 80% in just one hour

See if you can spot an AI deepfake with our test

Psychologist Dr Clare Sutherland is holding up two large photos. One shows the face of an Australian academic leading an international research study; the other is an AI-generated deepfake. Artificial intelligence has become so adept at creating realistic images, it is increasingly hard to figure out what is real or not. But can people be trained to spot an image of a human that has actually been created by a machine? That's a question Sutherland, from the University of Aberdeen, and her Australian colleague have been examining. But before we reveal the answer, have a go at this test - and note down your score. If you found that tough, you are not alone. It used to be far easier to spot computer-generated visual creations - often used by fraudsters - because AI would make blunders, like adding an extra finger or something else that was obviously weird. But AI learns from its mistakes. "Training on visual artifacts, like looking for a sixth finger or odd earrings, has had limited success, partly because the AI is getting too good, and fraudsters may avoid using pictures with obvious flaws anyway," explained Prof Amy Dawel. She is the woman with shoulder-length hair in the picture being held by Sutherland. The man's image is the fake. Dawel is the director of the Australian National University Emotions and Faces Lab. She has been leading a team of researchers in Australia, Canada and the UK to find out if people can be trained to rumble the AI imposters. The answer, for now at least, is yes - but learning to spot an AI fake requires a more subtle approach. Sutherland is leading the UK-based research at the University of Aberdeen. She said they had noticed they were getting a feel for which faces were real or AI just by looking at them. "So we thought, OK, it would be really interesting to see if we could teach other people this too," she said. For the experiments a pool of thousands of AI-generated faces was created using an AI image tool called StyleGAN3, one of the most realistic face generators available. Participants were tested before and after being given training The researchers trained participants in the studies by drawing their attention to six perceptual qualities: * Symmetry - AI often fails to recreate the quirks that make us human - a slightly drooping eyelid or a lop-sided smile. "If it's too good to be true, it probably isn't." * Proportionality - A similar concept. Very large noses or protruding ears are not typical of deepfake images. * Attractiveness - "AI faces tend to look more attractive," explains Sutherland. "That one is more subjective, an aesthetic judgement, but AI often creates faces that are pleasant looking." * Distinctiveness - "That could be something like 'what would make a face stand out in a crowd?' AI faces do tend to cluster towards the average. So they look a bit more generic." * Expressiveness - "AI faces tend to look less emotionally expressive", says Sutherland. "They tend to show less emotion." * Memorability - "They often look less memorable - they're difficult to remember." AI also tends to be less proficient at recreating non-white, older or younger faces because more of its training involves young white people. Some of these tips might sound quite similar and "fuzzy" - but that's the point. Rarely will you encounter a surefire "tell" that will unmask an AI fake. Rather, it is about becoming attuned to their characteristics and developing a gut feeling. Researchers found that by exposing people to images, both AI and real, then telling them which was which, they can get significantly better at it - even in the space of an hour or so. The researchers found the participants would typically increase their accuracy score from about 40% to 80%. A few individuals achieved close to 100% accuracy. Ironically, what the human brain is doing here is similar to the way that generative AI models work. Give them enough data to train on and, over time, their accuracy improves - even though we may not totally understand how they are doing it. The studies also looked at how confident the participants were at identifying the AI images. Previous research had indicated people were overconfident that they could spot AI faces, with the most confident people making the most errors. After training, participants were found to have increased their confidence in spotting the deepfakes. "That's helpful right?" says Sutherland. "Because if you don't know when you're correct or not, you can't really do anything with that information." OK, so are you ready to take another test? How did you do? Feeling more confident? If the answer is no, don't beat yourself up over it. In both the human world and that of generative AI, practice makes perfect - or at least a bit closer to perfect. There are many websites out there where, if you so desire, you can hone your skills. You can also volunteer to take part in the research yourself. The obvious danger is fraud. Global consultancy firm Deloitte has predicted that losses from AI deepfake scams in the US alone could rise to £40bn next year, up from £12bn in 2023. The report cited the example of a scam where an employee at a Hong Kong-based firm transferred £25m to fraudsters after a video call with a deepfake recreation of their boss. Another sinister use of deepfake technology is political espionage As long ago as 2019, an Associated Press investigation found that a LinkedIn profile - including a photo - belonging to a woman called Katie Jones appeared to be fictitious. Jones purported to be a Russia and Eurasia specialist with links to prominent Washington think tanks and policy circles. The AP report claimed she was actually a deepfake produced by Russian intelligence who had successfully connected with top US political aides and national security officials. In Australia, a politician is currently proposing a requirement to disclose and "watermark" AI-generated political content. To be fair to AI, Sutherland also sees some positive uses of the technology - such as the ability to quickly and cheaply show how a long-missing child might look at various ages. She says that if people are "engaging with it in good faith and people know that AI has been used, it could potentially be very useful for creative acts". So the good news is that we're yet not living in a dystopian world where it's impossible to tell what's real and what's computer-generated. The bad news is that AI models may have already "read" the published academic research papers. And it's learning.

Study finds AI-generated faces seem more trustworthy than real people

People trust faces created by artificial intelligence more than they trust photos of actual human beings, according to new research. The findings raise fresh alarm bells about online fraud, catfishing, and misinformation. The finding comes at a time when AI image generators have become accessible to virtually anyone, regardless of technical skill. That has put a powerful new tool for deception within reach of ordinary internet users rather than just specialists. The study, led by Alexis McGuire and a team from Lancaster University, is the first to examine how trustworthy people find AI faces created using the latest diffusion technology. This newer approach to image generation is more sophisticated than earlier AI systems. Spotting fake faces isn't easy Human beings are remarkably good at reading real faces, capable of forming judgements in as little as 100 milliseconds, a skill built up over the entire span of human evolution. But AI-generated faces have become so realistic that newer, more sophisticated systems can now fool people into thinking a fake face is genuine roughly a third of the time. That erodes an ability that once felt almost instinctive. To test this directly, researchers showed 169 participants a set of 96 faces spanning a range of races, genders, and ages. Researchers presented the faces in random order, and participants judged whether each one was real or AI-generated. The average accuracy came out to just 58.4%, only marginally better than flipping a coin. Oddly enough, participants rated faces from the newer diffusion model as less realistic than those from an older AI system known as a GAN. That was despite the diffusion model representing more advanced underlying technology. That result alone hinted that something more complicated than simple image quality was shaping people's judgments. AI faces win our trust A follow-up experiment pushed the question further. Researchers asked a new group of participants to rate the trustworthiness of 96 faces on a scale from one, very untrustworthy, to seven, very trustworthy. This approach separates the question of trust entirely from the earlier question of realism. Real human faces came out lowest, with an average trust score of 4.03. Both types of AI-generated faces scored higher across the board. GAN-produced faces averaged 4.36, while faces from the newer diffusion model scored highest of all, at 4.70. That means the AI faces people found least realistic in the first experiment were, somewhat bizarrely, the very ones they trusted most in the second. "This finding presents a paradox and thus highlights the possibility that realism and trustworthiness judgements are driven by two different psychological mechanisms," McGuire said. In other words, whatever process the brain uses to size up how believable a face looks may not be the same process it uses to decide whether that face seems safe or friendly. How scammers could benefit "Our research shows that people are at risk of being fooled by AI-generated images," McGuire said. "These AI models have democratized the online space, and they are accessible to anyone without technical skills who wants to create fake faces that can be used for a variety of harms." McGuire stressed the importance of educating the public about how easily people can generate convincing AI faces and the risks they pose, including misinformation, identity fraud, and catfishing. She also pointed to a broader concern that extends well past individual scams and stretches toward institutions and public life more generally. "As AI-generated images become more sophisticated and more accessible, as a society, we are increasingly exposed to AI-generated faces, often in nefarious and exploitative scenarios," said McGuire. "It is critical to understand the threat this democratization of generative AI brings, as well as to develop strategies to mitigate potential harms to individuals, organisations, and democracies." A problem with no easy fix The findings suggest that as diffusion models continue improving, the gap between how trustworthy AI faces seem and how trustworthy they actually are could keep widening. That gap gives scammers, catfishers, and disinformation campaigns a powerful opportunity to deceive people. What makes this especially concerning is the direction the technology seems to be heading. If newer AI models keep producing faces that people rate as increasingly trustworthy, simple visual instinct may become an increasingly unreliable defense against fabricated identities online. That could happen even if those faces do not necessarily look more realistic. Can you spot AI faces? The research team is continuing to study how people process real versus AI-generated faces. Anyone interested can take part in an ongoing anonymous online survey called "Examining Individual Differences in the Detection of Real and AI-generated Faces." Participants view a series of faces, rate whether each looks real or AI-generated along with their confidence level, and receive a score at the end reflecting how well they did. This will contribute significantly to a growing body of evidence about just how vulnerable human judgment has become in the face of increasingly convincing synthetic imagery. The study is published in the Journal of Vision. -- - Like what you read? Subscribe to our newsletter for engaging articles, exclusive content, and the latest updates. Check us out on EarthSnap, a free app brought to you by Eric Ralls and Earth.com.

What happens when AI detectors fail? Researchers say we must be trained to spot fake AI faces

Researchers say spotting AI faces may soon depend more on people than software Artificial intelligence has become remarkably good at creating fake human faces. So good, in fact, that the old tricks people relied on - counting fingers, spotting warped earrings, or looking for distorted backgrounds - are quickly becoming obsolete. According to a new study highlighted by the BBC, the next line of defence may not be a better AI detector at all. It might simply be a better-trained human. Researchers from the University of Aberdeen, working alongside Australia's National University, found that people can dramatically improve their ability to distinguish AI-generated faces from real ones after a relatively short period of structured training. Instead of hunting for obvious visual glitches, participants were taught to recognise subtle patterns that modern image generators still struggle to replicate consistently. The AI race is forcing humans to evolve too For years, identifying AI-generated images felt almost trivial. Early models often produced six fingers, mismatched earrings or impossible shadows. But today's generators, powered by systems such as StyleGAN3 and newer diffusion models, have largely moved beyond those tell-tale mistakes. As a result, researchers argue that relying on visual defects is no longer an effective strategy. Instead, participants were trained to judge six perceptual qualities that AI faces often share. These include unusually perfect facial symmetry, highly proportional features, above-average attractiveness, generic-looking facial structures, limited emotional expression, and faces that are surprisingly difficult to remember after you've looked away. Recommended Videos The results were striking. Before training, participants correctly identified AI-generated faces only around 40 percent of the time. After roughly an hour of guided learning and repeated exposure to both real and synthetic faces, accuracy climbed to nearly 80 percent. A handful of participants even approached perfect detection scores. More importantly, their confidence became better aligned with their actual performance, something earlier research suggested was often missing. Why spotting AI faces matters more than ever This isn't simply an academic exercise anymore. Deepfake technology is already being used in financial fraud, political influence campaigns and online identity scams. The BBC points to Deloitte estimates suggesting losses from AI-enabled deepfake fraud in the United States could rise to £40 billion next year, up sharply from around £12 billion in 2023. It also references a widely reported Hong Kong case in which scammers allegedly used a deepfake video call to convince an employee to transfer £25 million. Meanwhile, an earlier Associated Press investigation uncovered an AI-generated LinkedIn profile that successfully infiltrated US policy circles. The study also highlights another important issue: AI systems remain less reliable at generating older faces, younger faces and people from underrepresented ethnic groups because of biases in their training data. Those imperfections may still provide useful clues for human observers. Perhaps the most interesting takeaway is that the human brain appears to learn much like AI itself. By repeatedly seeing examples of real and fake faces, people gradually develop an intuitive sense of authenticity rather than relying on a single giveaway. Researchers believe that instinct may become one of our strongest tools as generative AI continues to improve. The irony is difficult to ignore. As artificial intelligence becomes better at pretending to be human, humans may have to start training themselves the way machines do - through data, repetition, and pattern recognition. AI detectors may keep improving, but the research suggests they shouldn't be the only defence. Human judgement still has a role to play; it just needs an upgrade.

Can you spot an AI image? Quiz shows how difficult identifying deepfakes has become.

Amy Corral is an award winning investigative journalist based in Los Angeles. She joined CBS News & Stations as a national investigative producer in 2022. A recent survey by an identity verification company found that Americans' ability to distinguish real images from AI-generated deepfakes was about as accurate as a coin flip. CBS LA Consumer Investigator Kristine Lazar put that finding to the test and scored just three out of 12 on an AI detection quiz before learning what to look for. "I've been a consumer investigator for nearly a decade, but I got an F when I tried to identify images and videos made with AI," Lazar said. Veriff, an online identity verification tool, created a quiz featuring side-by-side images and videos to test Lazar's ability to identify authentic and AI-generated content. Although she initially felt confident in her choices, she quickly discovered many of her assumptions were wrong. "I thought he was the fakest-looking one there," Lazar said after learning that one of the people she believed was AI-generated was actually real. Overall, Lazar correctly identified only three of the 12 images and videos, a 33% accuracy rate. Raul Liive, Veriff's product director, said those results are consistent with what the company found when testing the public. "We are seeing that people in America especially are close to, like, it's a coin flip essentially, that we can't really make a difference -- is it a fake photograph or a real one," Liive said. When asked whether people are guessing, Liive responded, "It's kind of a guess for you and me." Liive said the telltale signs people once relied on, such as distorted fingers, unnatural eyes or other obvious visual flaws, have largely disappeared as AI technology has improved. "The AI has improved over the last few years heavily," Liive said. "It used to be pretty simple because, as you said, fingers were missing, or eyes were weird, but right now, the quality is so good." Instead, he recommends closely examining facial features for subtle inconsistencies, unusual texture changes and unnatural patterns. Videos can be even more difficult because of continuous motion, but viewers may notice limited blinking, inconsistent movement speeds, or clothing patterns that blend unnaturally into the background. After receiving guidance from Liive, Lazar retook the quiz and improved her score from three correct answers to eight out of 12. Looking more carefully, she noticed subtle clues, including mismatched earrings on one subject. "Her earring's a little bit bigger on one side than the other side," Lazar said. "Her earrings aren't the same." Liive said even experts who work with AI every day are not able to identify every fake image correctly without additional tools. "No, I can't get 100% on that quiz," he said. The experience underscored how difficult it has become to rely on visual cues alone. "The key takeaway for me: Seeing is no longer believing," Lazar said. Experts recommend using AI-powered detection tools or specialized verification apps to help determine whether an image or video is authentic rather than relying solely on what appears on screen.