AI detector accuracy, explained

"Is it accurate?" is the first question everyone asks about an AI detector, and the honest answer is: very useful, never infallible. Here is how to read the score like someone who understands the tool.

The score is a probability, not a verdict

A detector compares your image against the statistical fingerprints it learned from millions of real and generated images. 92% doesn't mean "92% of this image is AI", it means the image resembles the AI side of that training far more than the real side. Scores near 50% mean the evidence is genuinely mixed, and honest tools show that instead of forcing a yes or no.

What causes false positives

Real photos can score high when they share properties with generated images: heavy beauty filters and skin smoothing, HDR processing, studio portraits with flawless lighting, upscaled or "enhanced" old photos, and highly stylized digital art. The detector isn't wrong that these look statistically synthetic, they've been processed toward the same smoothness generators produce.

What causes false negatives

Generated images can slip through when their fingerprint has been damaged: screenshots of screenshots, aggressive compression from being reposted across platforms, small thumbnails, added grain or filters, and images from brand-new generators the detector hasn't seen much of yet. Detection is an arms race, and the newest generators always lead briefly.

How to use the score responsibly

Treat extreme scores as strong evidence and middle scores as a prompt to look closer. Crop to the suspicious region for a cleaner read, check the visual tells yourself, and try to find the image's origin, a reverse image search often settles the question outright. Never treat any single score, from any tool, as proof on its own, especially before accusing a real person of faking something.