Provenance
Spotting AI images in your feed: checks that hold up
Quick answer
Visual tells such as odd hands or garbled text were never reliable and are fading fast. Checks that hold up are about evidence: a platform label, provenance data in the original file, earlier copies found by reverse image search, other sources for the same event, and the account that posted it. Combine two or three before you share.
Why visual tells stopped working
For a while generated images gave themselves away with extra fingers, melted jewellery or text that looked like letters but was not. Each new generation of models fixes more of these, and a single tell was never proof: real photographs have motion blur, odd reflections and bad crops too. Treat a visual oddity as a reason to check, not as an answer.
Checks that hold up
| Check | How | Limits |
|---|---|---|
| Platform label | Look for the network's AI or edited label | Missing labels mean little; labels can be wrong |
| Provenance data | Ask for the original file and read its credentials | Most shared copies have none |
| Earlier copies | Run a reverse image search | New images have no history yet |
| Other sources | Find other photos or reports of the same event | Takes time for breaking news |
| The account | Check history, location claims and links | New accounts can be genuine |
Meta has described labelling images when it detects industry-standard indicators such as C2PA and IPTC metadata, and asking users to disclose realistic generated media. That makes labels useful when present and weak evidence when absent. Provenance data is covered in checking Content Credentials before you share, and hidden marks in invisible watermarks on AI images.
Before you share
- If the image makes a claim about a real event or person, find a second source before sharing.
- If you share it anyway, say what you know and do not know in your own words.
- If it turns out to be generated, post the correction where the original share went.
Text in feeds is harder: short posts give detectors almost nothing to work with, as whether an AI detector can judge a social post explains. For images, the evidence-based checks above are about as good as it gets. The provenance section covers the same questions from the publisher's side.
Sources
- Meta, Our Approach to Labeling AI-Generated Content and Manipulated Media (April 2024)
- Google Search Help: search with an image
- IPTC NewsCodes: digital source type vocabulary
Questions
How can I tell if an image is AI-generated?
Combine evidence: a platform label, provenance data in the original file, a reverse image search for earlier copies and other sources for the same event.
Are extra fingers still a sign of AI images?
Less and less. Newer models fix most visual tells, and real photographs have oddities too.
Does reverse image search find AI images?
It finds earlier copies and similar images, which often reveals where an image came from. A brand-new image may have no history yet.
Do social networks label all AI images?
No. Labels depend on detected metadata or user disclosure, so many generated images carry none.
Hannah Voss, Editor. Checks every guide against a working WordPress install and the networks' current documentation. Last reviewed September 2026.
Related reading
- Invisible watermarks on AI images: what they can proveHow invisible watermarks on AI-generated images work, how they differ from Content Credentials, what a detected watermark proves and what it cannot.3 min read
- Checking Content Credentials before you share an imageHow to check an image's Content Credentials before you share or publish it: what a C2PA manifest shows, what a missing one means, and what to record.3 min read
- Can an AI detector judge a social post?Why AI text detectors are least reliable on the short, informal text of social posts and captions, what published tests found, and what to rely on instead.3 min read
- How platforms label AI content, and what triggers the labelHow major platforms decide to label AI-generated or altered media: creator disclosure, provenance metadata, detection, and how publishers avoid mistakes.4 min read