1. Look for the classic visual tells (still useful, less reliable each year)
- Hands and fingers — extra, missing, or fused fingers used to be a strong signal. Newer models get this mostly right, so absence of the flaw no longer proves a real photo.
- Text in the image — signs, labels, and background text are often garbled or nonsensical in generated images.
- Eyes and reflections — mismatched pupil shapes, inconsistent catchlights, or reflections that don't match the scene.
- Repeating patterns — fabric, foliage, or brick patterns that repeat unnaturally or blur into each other.
- Lighting and shadow direction — shadows that don't agree with a single light source.
- Edges around hair and small objects — soft, slightly "melted" boundaries where fine detail should be sharp.
Treat all of these as clues, not proof. The best current-generation models (mid-2026) get most of these right most of the time, which is exactly why relying on eyeballing alone is no longer enough.
2. Check the metadata
Real camera photos almost always carry EXIF metadata — camera make/model, focal length, shutter speed, sometimes GPS. Most AI generators don't produce this metadata, or produce generic/stripped metadata. But this check has a major caveat: screenshots, re-saves, and images shared through messaging apps (WhatsApp, Instagram, Telegram) strip metadata too — so missing EXIF is a hint, never proof on its own. A genuine photo re-shared a few times will often look metadata-free, same as a generated one.
3. Reverse image search
Run the image through Google Images or TinEye. If it appears on stock photo sites, news archives, or social media well before the date it was supposedly taken, that's strong evidence it's a real photo being reused — or a manipulated/mislabeled one, which is a different problem than AI generation.
4. Run an automated forensic check
This is where a tool like Pixoraid earns its place: it checks noise patterns, compression artifacts, and metadata simultaneously, and scores them into a calibrated probability instead of a gut feeling. It won't catch everything a human misses and it won't miss everything a human catches — the two approaches are complementary, not substitutes for each other. Read more about how Pixoraid's AI image detector combines these signals.
5. Know when to say "uncertain"
If a screenshot, a heavily compressed image, or a photo that's been re-saved multiple times doesn't give you a confident answer either way — that's the correct outcome, not a tool failure. High-stakes decisions (legal, journalistic, academic integrity) should treat any single detector result, human or automated, as one input among several, never the sole basis for a conclusion.
The same principles apply to video — see our AI video detector for a frame-by-frame breakdown instead of a single number.
Upload an image and get a probability score, verdict, and evidence report in seconds — free for your first 5 checks a month.
Try Pixoraid freeFrequently asked questions
Can you tell if an image is AI-generated just by looking at it?
Sometimes, especially with older or lower-quality generators. Current top-tier models (mid-2026) are good enough that visual inspection alone is unreliable for a confident verdict — combine it with metadata checks and an automated forensic tool.
What's the single most reliable manual check?
There isn't one reliable check on its own. Reverse image search catches reused/mislabeled photos; metadata catches some generated images; visual artifacts catch others. Combining multiple checks — including an automated evidence-based tool — is more reliable than any single method.
Do AI detectors work on screenshots?
Screenshots are harder to analyze because the re-encoding process destroys some of the forensic signal and always strips metadata. Pixoraid still runs a check on screenshots but is more likely to return 'uncertain' on them — which is the honest result, not a bug.