Freepik AI Detector
Stock libraries now carry both photography and generated imagery side by side, sometimes labelled and sometimes not. The distinction matters commercially: licensing terms, model releases and client requirements all differ depending on which one you actually downloaded.
Telling generated stock from photographed stock
Stock has conventions that make this slightly easier than the general case — and one thing that makes it harder.
No model release, no real model
A photographed stock portrait involves a real person and a signed release. Generated ones do not, which is precisely why they are cheap to produce at volume.
Faces that recur without recurring
Generated stock sets show people who look consistent within a set and exist nowhere else. A reverse image search on a photographed model usually finds other appearances; on a generated one it finds nothing.
Props and text that do not hold
Documents, screens, packaging and signage in generated business imagery degrade into plausible-looking nonsense at full resolution.
Too-clean studio conditions
Perfect lighting with no capture metadata behind it. Real studio photography leaves a full camera block.
The harder part: professional retouching
Photographed stock is heavily retouched, which strips exactly the skin texture and noise that mark it as photographic. This pushes some real stock towards a generated-looking profile — a genuine false-positive risk.
Library metadata
Downloads often carry the library's own identifiers, worth reading in [[/exif-viewer|the EXIF viewer]] before anything else.
How the analysis works
Upload the original file
Full resolution, straight from the source. Screenshots and re-saved copies destroy most of the evidence we measure.
12 independent methods run in parallel
Three neural networks look at the image as a whole. Nine forensic methods examine it as a signal: frequency (FFT) analysis, noise pattern, pixel-level statistics, EXIF metadata, Error Level Analysis, texture coherence, colour distribution, Benford's Law and edge detection.
You see the breakdown, not just a number
Every method reports separately, so you can tell whether the verdict rests on one weak signal or on nine that agree.
For images from any other source, start with our AI image detector — it covers any image type and runs the same twelve methods.
How much each method counts
Most detectors give you one number from one model. We run twelve independent analyzers and weight them — three neural networks carry the majority, but nine forensic methods examine the file as a signal and can overrule them. Here is the exact weighting we use in production:
| Method | Weight in final score |
|---|---|
| Neural network #2 | 25% |
| Neural network #1 | 15% |
| Neural network #3 | 15% |
| EXIF metadata | 10% |
| Frequency (FFT) | 6% |
| Benford's Law | 6% |
| Noise pattern | 5% |
| Error Level Analysis | 4% |
| Texture coherence | 4% |
| Edge detection | 4% |
| Pixel statistics | 3% |
| Colour distribution | 3% |
What people actually upload
These are our own numbers, not an industry estimate — every check run through FakeSec since launch. The split is closer than most people expect: roughly four in ten uploads really do turn out to be generated.
Who needs this
- Agencies — Client briefs that specify photography.
- Publishers — Editorial standards on generated illustration.
- Compliance teams — Advertising rules on synthetic imagery.
- Buyers — Confirming what a licence actually covers.
Accuracy — and where it breaks down
On our internal benchmark of 240,000 images (120,000 AI-generated, 120,000 real) the combined score reaches up to 98% on unedited, full-resolution files. That number drops, sometimes sharply, in four situations — and you should know them before trusting any result:
- Heavy compression. Social platforms re-encode everything. A photo pulled from Instagram has already lost much of its noise signature.
- Screenshots. A screenshot is a new image of an image. Original metadata and compression history are gone.
- Stylised content. Flat colour and clean lines remove the texture and noise evidence forensic methods depend on.
- Post-processing. Added grain, filters, upscaling and manual retouching all mask generation traces.
No detector on the market is 100% accurate — ours included. Treat a high score as strong evidence, not as proof. When a decision matters, look at which of the 12 methods agreed.
The honest caveat specific to stock: heavy professional retouching removes texture and noise, which are the same signals that mark an image as photographed. Expect a slightly higher false-positive rate on polished commercial photography than on ordinary photos.
Frequently asked questions
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