How to Tell If an Image Is AI-Generated

Updated July 1, 2026 · 6 min read

AI image generators like Midjourney, DALL·E, Stable Diffusion, Flux, and Grok can now create photos so realistic that a quick glance won't give them away. If you've ever looked at a viral photo, a dating profile, or a product listing and wondered whether it's real, this guide walks through exactly how to tell if an image is AI-generated — the visual tells to look for, the technical checks beneath the surface, and the fastest way to get a definitive answer.

1. Start with hands, fingers, and limbs

Anatomy is still the classic giveaway. AI models struggle to keep count of small, repeated structures, so look for hands with too many (or too few) fingers, fingers that bend the wrong way or merge together, and extra limbs peeking in from the edge of the frame. The same goes for feet, elbows, and anything a person is gripping.

2. Look closely at eyes, teeth, and ears

Faces are where AI gets uncannily close — and where the small mistakes hide. Check for:

• Irises or pupils that are different sizes, misshapen, or blurry
• Eyes with a glassy, "lights are on but nobody's home" look
• Teeth that overlap unnaturally or don't line up
• Asymmetric or mismatched earrings, and ears of slightly different shapes

3. Watch for waxy skin and "too-perfect" textures

AI-generated skin often looks airbrushed, plasticky, or flat — freckles and wrinkles get smeared into repeating patterns, and everything has a smooth, poreless sheen. Real photos carry messy, irregular detail; AI tends toward an over-polished perfection.

4. Follow the hair and fine details

Strands of hair are hard to render. Look for hair that fuses into the background, jewelry or glasses that melt into skin, and patterns (plaid, brick, tiling) that drift out of alignment or dissolve into mush on close inspection.

5. Read any text in the image

Text is one of the most reliable tells. Signs, labels, book spines, logos, and street signs in AI images are frequently garbled — plausible-looking letters that spell nonsense. If the words fall apart when you actually read them, that's a strong signal.

6. Inspect backgrounds and continuous lines

Backgrounds are where generators cut corners. A trick from image forensics: follow a straight line — a windowsill, a railing, a pipe, a wall trim — as it passes behind a foreground object. In AI images it often "breaks" and fails to line up on the other side. Watch for warped architecture, nonsensical objects, and scenes that look patched together from different places.

7. Check the lighting and shadows

Shadows in AI images frequently defy physics — falling at different angles as if there were several suns, or missing entirely. A common tell is a mismatch between the lighting on a person's face and the lighting of the scene behind them.

8. Question the overall "AI sheen"

Even flawless AI images often share a generic, over-produced quality: the anime character looks like a generic anime character, the city street looks like a generic city street. If an image feels strangely idealized, symmetrical, and free of the ordinary imperfections of real life, treat it with suspicion.

9. Look beneath the pixels: metadata and provenance

Not every clue is visual:

• Metadata (EXIF): real camera photos usually carry camera model, lens, and settings. AI exports often lack these — and some list the tool that made them.
• Content Credentials (C2PA): a growing standard that cryptographically records how an image was made; some tools embed it automatically.
• Invisible watermarks: systems like Google SynthID tag AI images at the pixel level. Absence of a watermark proves nothing (they can be cropped or stripped), but a present one is a clear signal.
• Reverse image search: if the "photo" has no history anywhere online, or only appears on a single suspicious post, that's a red flag.

10. Verify with an AI image detector

The manual tells are getting less reliable as generators improve — which is exactly what a detector is for. An AI image detector is a machine-learning model trained on millions of real and AI-generated images. It picks up on statistical fingerprints that generators leave behind — patterns invisible to the human eye — and returns a probability that the image is AI-generated.

One caveat worth knowing: a detector is only as good as the range of generators it was trained on. Tools trained narrowly can miss images from a generator they've never seen, while detectors trained on a diverse mix generalize far better. Use the score as a strong signal alongside the visual and metadata checks above — not as absolute proof.

The bottom line

There's no single trick that catches every AI image — and the obvious tells (hands, text) are fading fast as models improve. The dependable approach is to stack the signals: scan for the visual artifacts above, check the metadata and provenance, and run the image through a detector for a probability. Any one of these can be fooled; together, they're hard to beat.

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