How to tell if an image is AI-generated

There is no single reliable tell. That's the honest starting point, and anyone who gives you a five-second trick is selling something. What exists instead is a ladder of checks, from free-and-fast to rigorous, each catching things the others miss. This guide walks the ladder in order and is explicit about where each rung fails.

What you can check by eye

Visual inspection is the weakest rung, and it's getting weaker every year. The famous tells — six fingers, garbled text, melted jewelry — were real flaws of 2022–2023 generators, and current models have largely fixed them. Treat the absence of visual glitches as no information at all. That said, when a visual tell is present, it's still useful:

• Text and signage. Look at anything with letters: shop signs, book spines, jerseys. Generators have improved, but long or incidental text in the background still comes out mangled more often than foreground text.
• Geometry that has to add up. Follow straight lines: railings, tiles, window frames. Check that they stay straight behind an occluding object and come out the other side in the same place.
• Light and shadow. Pick two objects and ask whether their shadows agree on where the light is. Reflections — in mirrors, water, glasses, eyes — should contain a plausible version of the scene. Inconsistent reflections remain one of the harder things for generators to get right.
• Repeats. Crowds, leaves, bricks, fabric patterns. Generators sometimes tile subtle near-copies of the same face or texture patch.
• Too-clean surfaces. Skin without pores, hair strands that merge, teeth without individual edges — an overall airbrushed sheen at full zoom.

If you find one of these, you have a lead — not a verdict. Real photos contain weird geometry, bad lighting, and compression mush all the time. Which brings us to the rung people skip.

Check where the image came from, not just what's in it

Provenance checks are more reliable than pixel-squinting because they don't depend on the generator making a mistake:

• Reverse image search. Search the image (Google Lens, TinEye, Bing). If it appeared years earlier in another context, you've answered a different — often more useful — question: it's not what the caption claims, whether or not it's AI.
• Find the earliest posting. Who posted it first, and do they exist beyond this one image? A fresh account with one viral photo is its own signal.
• Metadata, with a caveat. EXIF data (camera model, timestamps) is stripped by most social platforms and is trivial to forge. Present metadata is weak evidence; absent metadata is no evidence of anything.
• Content credentials. Some cameras and editors now attach cryptographically signed provenance (C2PA). When present and valid it's strong; almost everything you'll encounter today doesn't carry it, and absence proves nothing.

Where statistical detection fits

AI generators leave statistical traces in pixel data — patterns that aren't visible to the eye but that a trained model can score. A detector reads the image and returns a score; the crucial part is how that score is turned into a verdict.

The number to demand from any detector is its false-positive rate: how often it calls a real photo AI. A detector that flags 27% of genuine messenger photos — which is what we measured from a public detector at its default threshold on our own benchmark — is worse than useless, because its alarms carry no weight. Our analyzer only issues verdicts at thresholds calibrated so that at most 5% (standard) or 1% (strict) of real photographs get flagged, and the report prints the cap it used.

That rigor has a price, and you should hear it from us: at the ≤5% false-positive setting, across our benchmark's conditions — including images that have been through WhatsApp and screenshots — the detector catches roughly 55–64% of AI images, not 99%. If a service quotes you a number like 99%, ask what it means — the answer is usually "on clean images from generators we trained on."

Two consequences follow, and they're printed on every one of our reports:

1. A flag is strong evidence. At the strict setting, a flagged image comes from a process that's wrong about real photos at most 1% of the time.
2. "Not flagged" is not "real." A calibrated detector deliberately lets borderline AI images pass rather than inflate false alarms. Absence of a flag is absence of evidence — never a certificate of authenticity.

What nothing can tell you

• No method proves an image is real. Detection can flag synthesis; it cannot certify authenticity.
• Brand-new generators are a moving target. A model released last week may not be represented in any detector's calibration yet, including ours.
• Small, cropped, and heavily edited images carry less signal. The less pixel data survives, the less any statistical method can say.
• Certainty isn't on the menu. Anyone offering a definitive yes/no — human expert or software — is overclaiming.

A practical order of operations

1. Reverse-image-search it. If it's old or recontextualized, you're done.
2. Look for the visual tells above — leads, not verdicts.
3. Check the source: earliest posting, account history, corroborating images.
4. Run a calibrated detector and read the verdict with its false-positive cap and its limits, not as a yes/no oracle.
5. If the stakes are high — money, safety, reputation, evidence — don't act on any single check. Combine them, and treat "unclear" as an acceptable answer.