Gemini Image Detector
Google's approach is different from everyone else's. Instead of relying on metadata that vanishes the moment a file is re-saved, Imagen and Gemini embed SynthID — a watermark hidden in the pixels themselves, designed to survive cropping, resizing and compression.
SynthID, and why it changes the problem
Every other marking scheme lives in metadata and dies with the first screenshot. Google put theirs in the pixels, which is a genuinely stronger idea — with one practical catch.
An invisible pixel watermark
SynthID is embedded in the image data itself and is designed to survive cropping, resizing, compression and filters — the exact operations that destroy ordinary metadata.
The catch: it is Google's to read
Verification runs through Google's own tooling. Third-party detectors, ours included, cannot read SynthID directly — which is why we analyse the pixel statistics instead of chasing a watermark we have no key for.
C2PA on newer models
Since the Gemini 3 Pro image models, Google output also carries Content Credentials alongside SynthID — readable in our free EXIF viewer while intact.
Its own rendering signature
Google models produce characteristic lighting and colour handling that our colour distribution and frequency analyzers pick up independently of any watermark.
Editing-model output
Gemini's image editing changes only part of a photo, leaving a region whose noise and compression history differ from the rest — our manipulation methods target exactly that.
No capture metadata
As with every generator: no camera, no lens, no exposure block.
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.
Since SynthID can only be verified by Google, Start with our independent 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
- Journalists — Images circulating from Google's consumer tools.
- Educators — Submitted work claimed as original.
- Platforms — Disclosure and labelling policies.
- Buyers — Product and listing photography.
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.
Honest limitation: we cannot verify SynthID — that requires Google's own tooling, and no third party can do it. What we do is independent forensic analysis of the pixels. If you specifically need a SynthID verdict, use Google's verification tool; for everything else, the twelve methods work regardless of origin.
Frequently asked questions
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