Midjourney Detector
Midjourney is the awkward case. Unlike DALL·E, Firefly or Imagen, it does not embed Content Credentials in its output — so checking the metadata gets you nothing. The only way to answer this is to look at the pixels.
Why Midjourney is harder to confirm
Most major generators now sign their output in one way or another. Midjourney does not, which removes the easiest check and leaves only the forensic one.
No Content Credentials
As of 2026 Midjourney does not embed a C2PA manifest, unlike DALL·E 3, Sora, Firefly and Imagen. A clean metadata result tells you nothing here — which is precisely why people end up on a page like this.
The house style
Midjourney has a recognisable look: dramatic lighting, shallow depth of field, a cinematic warm grade. Not evidence on its own, but a reason to check.
Detail that never rests
Ornamentation and micro-detail are distributed evenly across the frame instead of concentrating around the subject. Shows up in frequency analysis.
Physically impossible optics
Bokeh that does not match the implied focal length, highlights inconsistent with the light source, reflections that do not correspond to the scene.
Text that dissolves
Signs, labels and book spines resolve into letter-shaped forms that spell nothing — still one of the most reliable giveaways.
Uniform noise floor
No sensor to produce grain. What noise exists is statistically flat, which our noise analyzer measures directly.
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.
Not sure which generator you are dealing with? Start with our general 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 asks this
- Commission clients — You paid for original artwork.
- Contests and galleries — Entries where AI is banned or must be declared.
- Stock buyers — Licensing an image described as a photograph.
- Moderators — Platforms requiring disclosure of generated content.
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.
We do not claim to name Midjourney specifically over another diffusion model — anyone promising that is guessing. What we answer is whether the image was generated at all, and which of the 12 methods say so.
Frequently asked questions
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