How to tell if a photo is AI-generated: 7 checks anyone can run
Most guides on how to tell if a photo is AI-generated start with a detector and end with a percentage. That order is backwards. A score tells you how far a file sits from camera-original statistics, which is useful evidence and a terrible verdict. The checks below start with things you can see for yourself, then move to technical signals, and only treat a detector score as one input among several.
Knowing how to tell if a photo is AI-generated comes down to running several checks and reading them together. None of these checks prove anything on their own. What they do is tell you where to look next, and when to stop trusting a single number.
1. Look at hands, text, and repeating patterns
Generated images still struggle with three things: hands with clear finger counts, dense background text, and repeated structures like fence posts, window rows, or crowd faces. Open the image at full size and zoom in on each. Five fingers that merge into six, lettering that looks like a language but reads as nothing, or a row of windows where the spacing drifts are all worth flagging.
This check is fast and costs nothing, and it still works. It also fails often: modern generators get hands right most of the time, and a compressed screenshot destroys the fine detail you need.
2. Reverse-search the image
If the photo shows a real event, someone else probably published it first. Run a reverse image search and look at the earliest appearance. A viral image with no trace before last Tuesday is not automatically fake, but an image that appears in a dozen stock galleries is not breaking news.
Be careful with what you conclude here. A missing earlier copy can mean the image is new and real, or new and fabricated. Reverse search establishes provenance of publication, not of capture.
3. Check whether the metadata survived
Right-click the original file (not a screenshot) and inspect its properties. A camera original usually carries a make, model, and capture timestamp. Generative pipelines often write nothing, or write the tool's name.
Two failure modes matter more than the presence or absence of metadata. First, most messaging apps strip metadata on send, so absence is extremely common and proves nothing. Second, metadata is trivially editable, so a clean EXIF block is not a guarantee either.
4. Look for a C2PA manifest
C2PA, often surfaced as Content Credentials, is a signed record describing how a file was created and edited. If a manifest is present and the signature verifies, you learn the file's history. Major cameras, some editing tools, and several AI platforms now attach one.
The important limit: an absent manifest is not evidence of fabrication. Most of the images that reach a reviewer have passed through at least one tool that strips it. Absence tells you the record is missing, not that the image is fake.
5. Compare more than one detector
If you run a detector, run at least two — our methodology explains how the layers combine. Different models are trained on different data and weigh different artifacts. When one returns 99% and another returns 1% for the same file, that disagreement is the finding. It means the evidence is ambiguous and the case needs a human.
Averaging the two numbers into a single confident-looking score hides exactly the cases worth examining. Treat a high score as a reason to look closer, not a reason to stop.
6. Ask what changed between the original and the copy
The most common source of a wrong verdict is not a sophisticated fake. It is a real photograph that was brightened, cropped, and recompressed before it reached you. Editing shifts the same statistics that detectors measure, so a heavily processed real photo can score as synthetic.
If you can obtain the original file, compare them. If you cannot, say so in your assessment rather than implying the copy you inspected is the file that was captured.
7. Write down what you could not check
This is the step most people skip, and it is the one that makes a review defensible later. Record what you inspected, what was missing, and what remained ambiguous. A verdict that says "no provenance, no capture metadata, detector scores conflict, escalated to human review" is more useful than a confident label that turns out to be wrong.
What none of these checks can do
None of them prove an event happened or did not happen. Provenance records describe the file, not the scene. Detection scores describe statistical distance from camera output, not intent. Where provenance is missing and no original exists, the honest position is that the question is unresolved.
If you want to run the technical layers together on one file, start a free review and read the provenance, metadata, and detector output side by side. Sample reports show what a finished assessment looks like.