The most reliable way to tell whether an image is AI-generated is not staring at fingers or teeth. It is building an AI-generated image evidence chain across source, file history, metadata, watermarks, visual clues, context, and detector results.
For a quick first pass, upload the file to the EasyGlobe AI Image Detector. If the image will be used in news, ecommerce, ads, or compliance-sensitive material, complete the full seven-step workflow below.
If you are choosing tools for this workflow, compare our AI image detector tool guide, use the EasyGlobe AI Image Detector for a first pass, and review related AI articles.
How do you verify the image source first?
Source matters more than pixels. Record where the image came from, which account or site shared it, when it appeared, whether an original link exists, and whether an earlier version can be found. An image without a source chain should be treated cautiously even when it looks realistic.
If the image came from a social screenshot, ask for the original file. Screenshots remove useful file information and may break C2PA, EXIF, or watermark signals.
How do C2PA and Content Credentials help?
Use a Content Credentials viewer or C2PA-aware tool to check whether the image contains provenance credentials. The C2PA explainer describes how manifests, signatures, edit actions, and trust chains can be verified.
C2PA can tell you what credentials are present and whether they appear intact. It cannot prove that the event shown in the image is true, and credentials may be lost after screenshots, recompression, platform processing, or exports.
How can SynthID or platform watermarks help?
SynthID is an invisible watermarking technology. Google DeepMind explains that it embeds signals into AI-generated images so they can later be detected. OpenAI also documents C2PA and SynthID as provenance signals for generated images. OpenAI source note.
A platform watermark usually means the image may have passed through a specific generation system or toolchain. It does not automatically prove malicious use or cover every AI image source.
What metadata should you review?
EXIF fields, creation time, software fields, camera model, and export tools can provide useful clues. A photo claiming to be straight from a camera but missing camera data or showing an editing export deserves more review.
Metadata can be removed or modified easily. Social platforms often strip it during upload. Treat it as one signal, not the final answer.
How do reverse image search and context checks help?
Run reverse image searches to look for earlier versions, similar compositions, source photos, or reposts. Many fake-image incidents are actually old photos with new captions, partial edits, composites, or real photos described incorrectly.
Also verify places, weather, signs, architecture, clothing, plates, timeline, and event context. AI images can fail in details, but real photos can also look odd because of compression, motion blur, or perspective.
Which visual clues still matter?
Common clues include garbled text, repeated textures, inconsistent reflections, unnatural shadows, merged glasses or earrings, zipper artifacts, unusual hands, and strange edges. But visual inspection is becoming less reliable as image models improve.
Use visual clues to raise questions, not to make a final claim. Write them down as review notes, then verify them against source, watermark, metadata, and context evidence.
How should you write an AI-generated image risk conclusion?
For AI-generated image detection, use more than one detector. Record each result, threshold, file version, and timestamp. Do not rely on one screenshot of an AI probability score because tools differ in training data, thresholds, and explanations.
Microsoft Research explains that provenance, watermarking, and fingerprinting methods each have strengths and limits. Use detector results as evidence, not an automated verdict.
What decision template should reviewers use?
Write the conclusion in three parts: whether the image source and original file are confirmed; what C2PA, SynthID, metadata, and detectors show; and the current risk level plus whether the image can be published, listed, or shared.
Example: The image has no confirmed original source, no trusted C2PA credential was detected, metadata appears stripped by a platform, and two detectors flagged medium-high risk. The conclusion is not “definitely AI-generated”; it is “source is insufficient and generated-image risk exists, so publication needs the original file or direct confirmation.”
FAQ
Can hands and teeth still reveal AI images?
They can provide clues, but they are not enough for a final conclusion. Newer models handle hands, text, and lighting better, while real photos can also show artifacts from blur or compression.
Does missing C2PA mean an image is fake?
No. Many real images do not include C2PA, and many platforms remove or damage credentials. Missing C2PA only means that this type of provenance evidence is unavailable.
When is human review required?
Use human review when images involve news, politics, health, finance, ecommerce products, advertising claims, or account penalties. Detector results should be review material, not the final decision.