How it works
A good AI picture check combines automated detectors, visual inspection, metadata review and provenance research. Each signal can be useful. None of them is perfect alone.
Core idea
Most tools are trained on large sets of camera photos, edited images and AI-generated examples. When you upload a picture, the detector estimates whether the file resembles known generated images more than ordinary photos. That estimate is usually shown as a confidence score.
A classifier learns statistical differences between human-captured images and images from generators such as Midjourney, DALL-E, Stable Diffusion, Firefly, Flux or other systems. It does not know the truth; it compares patterns.
AI images can contain overly smooth skin, repeated background details, inconsistent micro-texture or edges that look sharp in one area and melted in another. Detectors can use these patterns as input features.
A high AI score means “this looks similar to synthetic examples in the detector’s training data.” It does not prove intent, authorship, copyright status or whether a human later edited the image.
Technical signals
Different tools use different models, but these are common categories behind AI-generated image detection.
The detector reviews color transitions, local contrast, noise, edges and the way detail changes across the frame. Real sensors, lenses and compression pipelines often leave different patterns than image generators.
Some systems transform the image into frequency space to look for hidden regularities. Diffusion denoising, upscaling and resampling can leave subtle signals that are not obvious at normal zoom.
Generated images may contain wrong fingers, impossible object connections, unreadable text, inconsistent reflections, broken logos or background objects that make no physical sense.
Social platforms recompress images. Screenshots, crops and filters can destroy useful signals or create new artifacts that confuse detectors.
Metadata can show camera model, editing software, timestamps or creator credentials. But metadata is often stripped by apps and can sometimes be edited, so absence is not proof.
Some images include signed provenance data that records creation or editing history. This can help, but many images online still have no reliable provenance trail.
Why results disagree
Disagreement is normal. A detector trained on one set of generators may miss another model. A tool tuned for deepfakes may behave differently from one tuned for product images or artwork.
| Problem | What happens | What to do |
|---|---|---|
| New generator | The detector may not have seen enough examples from the latest model. | Use multiple tools and rely more on source history. |
| Edited image | Human edits, inpainting, filters or upscaling can change the signal. | Find the oldest available version and compare variants. |
| Screenshot | Interface compression and resampling can hide original clues. | Ask for the original file when the decision matters. |
| Stylized real photo | Heavy retouching, HDR, beauty filters or CGI-like lighting can look synthetic. | Check photographer, shoot context and other images from the same set. |
Practical workflow
This workflow is slower than uploading to one detector, but it is much safer for journalism, schools, marketplaces, client work and sensitive claims.
Save the highest-resolution file and note where you got it. Do not rely on a cropped social preview.
Use at least two detector websites. Treat disagreement as a review flag, not as a tie to break casually.
Zoom into text, hands, reflections, shadows, faces, logos, background objects and repeated textures.
Use reverse image search, archive pages, uploader history, disclosure notes and related images from the same scene.