Humans vs. Machines: Microsoft Finds People Only 63 % Accurate at Spotting AI Images

Deepfake images have become ubiquitous, from realistic celebrity “photos” to viral political memes. But just how good are humans at spotting them? On July 30 2025, TechSpot reported on a Microsoft study in which more than 30,000 participants around the world played a web‑based game called Real or Not?. The results were sobering: participants correctly identified AI‑generated images only 63 % of the time, not much better than chance. By contrast, Microsoft’s own AI detection tool achieved 95 % accuracy. The study underscores the challenges of spotting deepfakes, particularly those created with generative adversarial networks (GANs), and raises concerns about misinformation in an era of powerful image synthesis.

What Actually Happened?

The Announcement

Microsoft Research launched an online quiz at realornotquiz.com inviting users to determine whether images were real or generated by AI. Over 30,000 people participated, producing a significant dataset. TechSpot summarised the results, noting that the average human score was 63 %, while Microsoft’s detection tool, which looks for tell‑tale artifacts and inconsistent pixel patterns, scored 95 %. The study found that images generated by GANs were particularly deceptive, fooling participants more often than diffusion‑based images. It also revealed that participants sometimes misclassified real photos as fake, suggesting that deepfake paranoia could lead people to doubt authentic content.

What’s New?

While numerous studies have examined deepfake videos, this research focuses on static images. It highlights how generative AI has advanced to the point where still images — a format long considered easier to fake — are nearly indistinguishable from reality. The 63 % accuracy rate suggests that even highly aware individuals cannot reliably spot fakes without assistance. The study also compared various AI models and found that diffusion models (similar to DALL‑E or Midjourney) occasionally produce obvious artifacts, whereas GANs excel at producing realistic textures that mimic low‑quality photographs. This nuance helps researchers understand which generation techniques pose the greatest threat.

Behind the Scenes

The Real or Not? quiz is part of Microsoft’s broader effort to build “AI for good” tools that can detect and flag synthetic content. The company’s detection algorithm uses a combination of pixel‑level analysis and deep learning to identify patterns humans miss. It looks for irregularities in lighting, texture and noise distribution — features often overlooked by the human eye. According to the TechSpot report, the tool’s accuracy reached 95 %, demonstrating that machines can outperform people in detecting fakes.

The research also provides insights into human perception. Participants tended to be more skeptical of highly polished images, correctly assuming they were AI‑generated. Conversely, photos that looked like imperfect snapshots — slightly blurry, poorly lit — were more likely to fool people. Microsoft plans to use the feedback from the game to improve both its detection algorithms and public education campaigns.

Why This Matters

Everyday users are constantly bombarded with images on social media, many of which may be synthetic. The fact that people could only detect AI fakes 63 % of the time means misinformation campaigns can easily take hold. Believing a fabricated image could sway public opinion, lead to reputational harm or even incite violence. Tools that help consumers verify authenticity are therefore essential. Microsoft’s detection tool — and similar ones being developed by other companies — could be integrated into social media platforms, warning users when an image is likely fake.

Tech professionals should note that the arms race between generators and detectors is intensifying. As models like GANs improve, detectors must adapt. Understanding which generation techniques produce more convincing fakes can guide researchers in designing better detection algorithms. Additionally, this research highlights the importance of responsible AI: if companies release powerful generative tools, they must also invest in countermeasures.

For businesses and startups, the results are a reminder that brand image and customer trust can be undermined by deepfakes. A forged image of a product defect or controversial incident could go viral before the truth emerges. Companies may need to deploy AI verification tools internally and monitor social media for synthetic content. Startups could find opportunities in building detection services, browser extensions or digital watermarking technologies.

From an ethics and society perspective, the study raises questions about consent and representation. Synthetic images can be used to create non‑consensual content, such as fake pornography, or to fabricate evidence. Ensuring that generative AI is used responsibly requires regulation, transparency and public awareness. Education campaigns that teach people how to critically evaluate images can complement technical solutions.

X.com and Reddit Gossip

The Real or Not? quiz quickly went viral on social media, with users comparing scores and bragging about their ability (or inability) to spot fakes. On r/accelerate, a user posted their results: “I made 8 tests so far, grade score: 1‑80 %, 2‑67 %, 3‑87 %, 4‑87 %, 5‑100 %, 6‑70 %, 7‑87 %, 8‑93 %… Total score 87.63 %, good to know I’m not alone!” Others admitted that faces were particularly tricky: “I only got one wrong. That one was a very zoomed‑in portrait and I couldn’t find anything wrong with it. Faces are harder to spot,” another commenter wrote. Some participants shared screenshots of their poor performances, joking that they would “be doomed and unable to differentiate AI pics” in the future.

On X, the hashtag #RealOrNot trended for hours. One tweet quipped: “My brain after playing Microsoft’s Real or Not? quiz: ‘I trust nothing!’” Another thread pointed out that GAN images often look like low‑quality photos from the early 2000s, which makes them more believable. A few skeptics dismissed the quiz as a marketing stunt, arguing that detection tools should be open sourced rather than proprietary. The broader conversation reflected anxiety about the erosion of visual truth and excitement about AI’s creative potential.

Related Entities and Tech

• Microsoft Research: Developer of the Real or Not? quiz and the AI detection tool.

• Generative Adversarial Networks (GANs): Models that pit two neural networks against each other to produce realistic images, often harder for humans to detect.

• Diffusion models: AI systems that generate images by gradually denoising random noise; some produce noticeable artifacts.

• Digital media literacy organizations: Groups like the Poynter Institute that promote critical evaluation of media.

Key Takeaways

1. Humans Struggle to Spot Deepfakes: Participants in Microsoft’s quiz identified AI‑generated images only 63 % of the time.

2. AI Outperforms People: Microsoft’s detection tool achieved 95 % accuracy, showing that machines can already surpass human ability to spot fakes.

3. GANs Are Especially Deceptive: The study found that GAN‑generated images were more convincing than those from diffusion models.

4. Trust in Images Is Eroding: Misclassifying real photos as fake shows that deepfake paranoia could lead to skepticism of authentic content.

5. Need for Education and Tools: Detection algorithms and media literacy campaigns are essential to combat misinformation in a world of ubiquitous AI imagery.

6. Social Media Debate: Online discussions ranged from humorous acceptance (“I trust nothing now”) to calls for open‑source detectors and stricter regulation of generative AI.

The Real or Not? study is a wake‑up call. As synthetic images become more realistic, we will need a combination of technology, regulation and public awareness to preserve trust in visual media. Until then, remember: if an image seems too outrageous to be true, it just might be AI.