How to Tell If an Image Is AI-Generated: 6 Checks Compared (2026)

How to Tell If an Image Is AI-Generated: 6 Checks Compared (2026)

Last updated: September 26, 2026 · By the sparkpix.ai Editorial Team

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Quick answer: check the file before you check the picture. If the original file carries C2PA Content Credentials or an IPTC "trained algorithmic media" label, the tool that made it has already told you. If those labels were stripped, as they usually are after a screenshot or social upload, combine a reverse image search, a look at who posted it first, and a pixel-based AI image detector. No single check is proof; two that agree usually are enough to act on.

Spotting AI images by eye was easy in 2023: six fingers, melted text, earrings that did not match. In 2026 that is no longer a reliable test. GPT Image 2, Gemini and Midjourney render hands and short text correctly most of the time, and the images that fool people are the plain ones: a flooded street, a dented car, a café selfie.

This guide compares the six checks that still work, what each one catches, and where each one fails.

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The six checks at a glance

Check	What it catches	Where it fails	Time	Cost
1. Provenance metadata (C2PA / IPTC)	Images from tools that label their output (OpenAI, Adobe, Google, Microsoft and other C2PA members)	Screenshots and social uploads strip it	Seconds	Free
2. Watermarks	Visible logos; invisible marks the maker can read	Cropped, edited, or no public reader	Seconds	Free
3. Reverse image search	Real photos reused out of context	Brand-new AI images have no history	1 minute	Free
4. Visual clues	Older or careless generations	Current models make few visible mistakes	1–3 minutes	Free
5. Context and source	Fake accounts, impossible events	Takes judgment; slow	5+ minutes	Free
6. Pixel-based AI detector	AI images even with metadata stripped	Probability, not proof; newer generators are harder	2–3 seconds	Free first check on sparkpix, then 3 credits

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1. Check the provenance metadata

What it is. C2PA (the Coalition for Content Provenance and Authenticity) is an open standard for attaching signed "Content Credentials" to a file: which tool made it, and whether AI was involved. Separately, the IPTC photo metadata standard has a Digital Source Type field, and the value trainedAlgorithmicMedia means "made by a generative model". OpenAI, Adobe, Google, Microsoft and other members of the coalition write one or both into images their tools produce.

How to check. Upload the original file to a Content Credentials viewer, or to an AI image detector that reads C2PA and IPTC before it analyses anything else. When the label is present, it is the most decisive answer you can get: the maker declared it.

Where it fails. Metadata lives in the file, not in the pixels. Most social platforms and chat apps strip it on upload, and a screenshot creates a new file with none. A missing label therefore tells you nothing. Also note that Content Credentials prove provenance, not AI use: a camera or an editing app can sign a real photo too.

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2. Look for a watermark

Some generators stamp a visible logo in a corner of the image. That one is easy to spot, and just as easy to crop out.

Invisible watermarks are harder to remove, but they can usually only be read by the company that embedded them. Google marks images from its generators with SynthID, for example, and there is no public detection API for it. So an invisible watermark mostly helps the platform that made the image, not you.

Verdict: worth a glance, rarely decisive.

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3. Run a reverse image search

Search the image with Google Lens, TinEye or Bing Visual Search. This is the best way to catch a real photo used to tell a false story: a 2019 flood passed off as last night's storm, or a stock photo used as a dating profile.

It is much weaker against AI images. A face or scene generated this morning has never been posted anywhere, so the search usually comes back empty, and an empty result is exactly what a brand-new real photo returns too.

Verdict: essential for recycled photos, close to useless for fresh AI ones.

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4. Look for visual clues

Zoom in and check the places generators still get wrong:

• Text on signs, labels and screens: long or small text is where letters still drift.
• Hands, teeth and jewellery: better than they were, still worth a look.
• Reflections and shadows: does the mirror show the same room? Do the shadows fall one way?
• Background objects: railings that merge, cars without doors, crowd faces that blur into each other.
• Surfaces that are too perfect: skin with no pores, food with no crumbs, light with no harsh edge.

Where it fails. Current generators make far fewer of these mistakes, and real photos can have odd reflections or heavy retouching. Visual clues are a reason to keep checking, not a verdict. If you want to test how good your eye really is, try the daily Real or AI game: two photos, pick the real one.

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5. Check the context

Most fakes are caught by asking where an image came from, not by studying the image:

• Who posted it first? A news photo with no photographer, agency or second angle is a warning sign.
• How old is the account? Scam profiles are often new and have few, very polished photos.
• Is there camera data? Real photos often carry the camera or phone model in their EXIF data. It can be stripped or faked, so its absence is not proof.
• Does the claim need the photo to be true? Insurance claims, refund requests, rental listings and breaking news are where AI images do the most damage.

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6. Use a pixel-based AI image detector

When the labels are gone, a classifier can still look at the pixels themselves (texture, noise, structure) and estimate how likely the image is AI-generated. This is the only check that works on a screenshot of a brand-new image.

We built one into sparkpix. The sparkpix AI image detector runs two layers: it reads C2PA and IPTC labels first, then scores the pixels. Here is what we measured.

	Result
Test set	14 AI images from GPT Image 2 and Gemini + the 14 real photos they were modelled on
Correct	27 of 28
Real photos flagged as AI	0 of 14
AI images passed as real	1 of 14
Typical time per check	2–3 seconds
Cost	First check free on sign-up (5 credits, a check costs 3); then $9.99 for 350 credits, about 116 checks

The result is an AI percentage with a verdict band: 85–100% AI Generated, 60–84% Likely AI, 40–59% Uncertain, 16–39% Likely Real, 0–15% Real Photo.

Where it fails. A detector gives a probability, not proof. Our test set is small and covers two generators. Newer models can be harder to catch, and heavily filtered real photos can score higher than they should. Read a score in the Uncertain band as "unknown", and never act on a detector alone when someone's reputation or money is at stake.

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Which check should you use?

Situation	Start with	Then
A viral news photo	Reverse image search, then who posted it first	Pixel detector
A dating or social profile	Pixel detector (a generated face has no search history)	Account age and other photos
An insurance or refund claim	Ask for the original file, then check the metadata	Pixel detector
A rental or marketplace listing	Reverse image search	Pixel detector; insist on a video tour
A photo contest or stock submission	Metadata on the original file	Pixel detector for anything unlabelled

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Summary

1. The original file is worth more than any screenshot, because it may still carry C2PA or IPTC labels.
2. Reverse image search catches recycled real photos, not fresh AI ones.
3. Visual clues still help, but a clean image is no longer evidence that it is real.
4. A pixel-based detector is the only check that works once the labels are gone. Treat its score as one strong signal, not proof.

Want to check an image now? Upload it to the AI image detector; your first check is free when you sign up.

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Sources: C2PA technical specification (c2pa.org); IPTC Photo Metadata Standard, Digital Source Type vocabulary (iptc.org); sparkpix.ai detector test of 28 images, September 2026.