Ideogram Detector
For years the reliable trick was simple: if the text in the image is gibberish, it is AI. Ideogram broke that. It renders clean, readable typography — which means the single most-repeated piece of advice on the internet no longer works.
When readable text stops being proof
Ideogram specialises in typography, so the usual advice fails. Everything else still applies — and matters more.
Correct text, generated image
Clean lettering on a poster or logo no longer indicates a human designer. This alone makes older detection advice obsolete.
Typography that is too even
Kerning and baselines are mathematically regular in a way hand-set type and photographed signage rarely are.
Layout without a grid
Real design follows an underlying grid. Generated compositions approximate one, with elements that almost align.
Flat generation statistics
Unchanged by good typography: noise, frequency profile and texture coherence still read as generated.
No capture data
A photographed poster carries camera metadata. A generated one carries none.
Edges of letterforms
Rendered type has anti-aliasing that differs from ink on paper photographed under real light — visible to our edge analyzer.
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.
If you are not sure which tool produced the image, 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 checks this
- Designers and clients — Logo and poster work billed as original.
- Print shops — Artwork submitted for production.
- Brand teams — Unauthorised generated material using your identity.
- Contests — Design competitions with AI restrictions.
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.
Worth restating plainly: readable text in an image is no longer evidence of human authorship. If you have been using that rule, it has been out of date for a while.
Frequently asked questions
Related tools
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