Step 1: Begin with the claim
Write down what you are trying to establish before examining pixels. “Was any AI used?” differs from “Did this event happen?” or “Is this an unedited camera capture?” A generated illustration can be openly disclosed, and a camera image can carry a misleading caption. Clarifying the question prevents a score from answering something it cannot measure. Avoid framing a review as an accusation about a named person.
Step 2: Get the best available source
Prefer the original export to a screenshot, preview or copy pasted into a document. Record the source link and any production explanation, and look for earlier copies of the image to find where it first appeared. Ask the creator whether the file was generated, photographed or edited, and which changes were made. Respect permission and privacy when requesting supporting material; investigating a file's origin does not require identifying the people in it.
Step 3: Check Content Credentials
Open the original file in a C2PA validator, such as the Content Authenticity Initiative's Verify tool, or check it in Gemini, which can read Content Credentials. A validated manifest can record whether a file came from a camera, an AI generator or an editing app. A manifest is meaningful only once its signature and signer are validated, and most shared images carry none, so absence is common and inconclusive.
Step 4: Check for an invisible watermark
Some generators embed invisible watermarks. Google's SynthID can be checked by uploading a file to the Gemini app and asking “Is this made with AI?”, and OpenAI previews a verifier for its own provenance signals. Each tool only detects the signals it supports, so a negative result rules out nothing beyond that coverage.
Source: Google: identifying AI-generated media (opens in a new tab)
Step 5: Read the metadata
A metadata viewer such as ExifTool shows camera fields, software tags and IPTC fields. The IPTC Digital Source Type code trainedAlgorithmicMedia is the standard value for media created by a generative AI model, and compositeWithTrainedAlgorithmicMedia marks AI-assisted edits such as inpainting. Some local generators also save prompts in PNG text. Metadata is easy to edit or strip, so treat it as a clue; our metadata guide covers the details.
Source: IPTC Digital Source Type vocabulary (opens in a new tab)
Step 6: Use visual clues as questions
Look for inconsistencies in perspective, object relationships, reflections or text in the scene, but treat those observations as tentative. Camera optics, compression, artistic choices and ordinary editing can also create odd details. Conversely, convincing details do not guarantee photography. If the image claims to show a news event or a product, compare it with independent records of that specific subject instead of relying on an aesthetic checklist.
Step 7: Weigh any detector score
If you use an AI image detector, check what it was tested on: generator coverage, transformed inputs, false-positive rate, detection rate and how often it declines to answer. Detectors trained on older generators can miss newer ones, and compression can push scores either way. If independent evidence stays weak, keep the conclusion uncertain and note what would help. Never penalize a person solely because a tool assigns a high number to their image.
Is there a reliable way to tell if an image is AI-generated?
There is no single reliable test. The strongest evidence is a validated provenance record or watermark from the tool that made the file, together with the original source. Visual inspection and detector scores help prioritize questions but are error-prone on their own, especially for edited photos and newer generators.
What are common signs of an AI-generated image?
Older generators often produced garbled text, malformed hands, mismatched accessories, impossible reflections or inconsistent shadows. Newer models make far fewer of these errors, and real photos can show similar oddities from lenses, motion or compression. Use such signs as reasons to look for the source, not as proof.
Can AI image detectors be wrong?
Yes. Detectors produce false positives on real photos and miss AI images, particularly images from generators missing from their training data or files that have been compressed, resized or screenshotted. Our guide to detector accuracy explains how to read accuracy claims and why base rates matter.