Generative image models can produce convincing portraits, product photos, news-style images, and artwork in seconds. Some mistakes remain obvious, but many generated images no longer have the distorted hands or unreadable text that once made them easy to spot.
A stronger review combines four things: the image itself, its source, available provenance information, and an image detector. No single clue works in every case.
Begin with the source, not the pixels
Ask where the image first appeared. A screenshot reposted across several accounts has less useful context than a file published by a known organization with a photographer, location, and date. Search for earlier versions and check whether reputable sources show the same event from other angles.
Reverse-image search can reveal whether the picture is old, cropped, or paired with a false description. Even when an image is genuine, its caption may not be.
Look for inconsistencies that affect meaning
Zoom in and inspect areas where the model must maintain structure across the frame:
- Text on signs, packaging, clothing, screens, and documents.
- Reflections in mirrors, windows, water, and polished surfaces.
- Repeated objects, background faces, jewelry, and fine patterns.
- Lighting direction, cast shadows, depth of field, and perspective.
- Edges where hair, glasses, hands, or tools meet another object.
These clues can support a review, but they are not proof. Compression, portrait-mode processing, image restoration, and ordinary editing can create similar artifacts.
Check provenance when it is available
Some publishers and creation tools attach signed provenance records known as Content Credentials. The Coalition for Content Provenance and Authenticity (C2PA) develops a standard for recording how a digital asset was created and edited.
A valid record can provide useful evidence about origin and changes. Missing credentials do not prove that an image is fake, however. Many cameras, platforms, and editing workflows do not yet preserve them.
Run an image analysis
Stealth AI Detector accepts JPG, PNG, and WebP files. Choose the highest-quality version you can access, upload it in the Image Detection screen, and review the AI and human probability signals returned by the app.
Use a layered conclusion
Instead of labeling an image “real” or “fake” from one clue, record what each layer shows:
- Source: Is the publisher identifiable and credible?
- Context: Do other records support the event or subject?
- Provenance: Is a trustworthy creation or edit history available?
- Visual review: Are there meaningful structural inconsistencies?
- Detector result: What probability signals does the file produce?
When several independent layers point in the same direction, confidence increases. When they conflict, the correct conclusion may be that the image remains unverified.