Dating Scam Photo Checker
Romance fraud almost always starts with a face that is not theirs. If money has come up, or the story has started to feel strange, checking their photos is the fastest reality check available — and the person on the other end never knows you did it.
The pattern romance fraud follows
Romance scams are run at scale from scripts, and they follow a recognisable sequence. If several of these fit, check the photos before anything else happens.
A profession that explains everything
Offshore engineer, military deployment, surgeon abroad, long-haul crew. The job conveniently explains why they cannot meet or video call.
Intensity, fast
Declarations of love within days or weeks, often before a single live conversation. Manufactured attachment is the whole mechanism.
Every video call fails
Broken camera, terrible connection, a base that forbids it. There is always a reason, and there is always another one next time.
A crisis that needs money
Hospital bill, customs fee, blocked account, a flight home. Small at first, then larger — the first request is a test of whether you will pay.
Payment channels with no recovery
Crypto, gift cards, wire to a third party. Chosen specifically because the money cannot be traced or reversed.
Intimate photos requested
Often followed by blackmail. Sometimes the images they send are deepfakes of someone else entirely.
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.
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.
What to do, in order
- Check the photos first — It is quick, free, and they cannot tell.
- Reverse image search too — Catches stolen photos that forensics cannot flag.
- Send no money and no documents — Not one payment, no passport scan, no bank details.
- Keep everything — Messages, photos, payment records — this is your evidence later.
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.
If the check comes back clean, that does not mean the person is who they say. It means the photo probably came from a camera — it could still be a real photo of someone else entirely. The behavioural pattern above matters more than any single technical result.
Frequently asked questions
Related tools
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