Best AI Image Detector: How to Compare
Every detector claims 98% accuracy. Almost none of them say on what, against which generators, or how they handle a compressed screenshot from Telegram. This page is the checklist we would use ourselves — including the parts where we come off worse.
Six questions worth asking before you trust any detector
Marketing numbers are close to meaningless in this category. These are the questions that actually separate a usable tool from a confident-looking guess:
What is the accuracy measured on?
A detector tested only on clean Midjourney output will report a beautiful number and fall apart on a compressed screenshot. Ask for the test set, not the headline figure.
Does it use one model or several methods?
A single classifier gives one opinion. When it is wrong, nothing catches it. Multiple independent methods can disagree — and that disagreement is information you want to see.
Does it show you why?
A bare percentage is not evidence. If a decision matters, you need to know which signals fired — metadata, compression history, noise, frequency — and whether they agreed.
How does it handle uncertainty?
A tool that always answers confidently is hiding something. Real forensic analysis produces inconclusive cases, and saying so is more useful than a coin flip presented as a verdict.
What happens to your file?
Whether uploads are stored, for how long, and whether they feed training data matters if you are checking something private or legally sensitive.
Can you get to it without a sales call?
Several strong engines are enterprise-only: you contact sales, negotiate a contract, then get access. Fine for a platform, useless when you need to check one photo today.
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.
The main tools side by side
Only publicly documented properties, no invented benchmark numbers. Where we are the weaker option, it says so.
| FakeSec | Hive Moderation | Illuminarty | Sightengine | |
|---|---|---|---|---|
| Access | Free account, self-serve | Enterprise, via sales | Self-serve | Self-serve + API |
| Free usage | 3 checks on signup | No self-serve free tier | 5 scans per day | Free tier for testing |
| Use without registering | No — account required | No | Yes | No |
| Paid entry point | Token packs, from small | Custom contract | From $10/month | From $29/month |
| Methods behind the score | 12 (3 networks + 9 forensic) | Proprietary models | Model + heatmap | Proprietary models |
| Per-method breakdown | Yes | No | Heatmap of regions | Category scores |
| Video | Yes, frame analysis | Yes | Images and text | Yes |
| Telegram bot | Yes | No | No | No |
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.
Which one fits which job
- One photo, right now — Any self-serve tool. An enterprise API is the wrong shape for a single check.
- Moderating a platform at volume — An enterprise API with a contract and an SLA will serve you better than any consumer tool.
- A decision you may have to defend — Whatever you use, insist on a per-method breakdown — a single percentage will not survive scrutiny.
- Regular, occasional checking — Look at cost per check rather than the monthly price. Subscriptions lose to packs at low volume.
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.
For the sake of consistency, our own weak spots: we require an account before the first check, we do not publish a public third-party benchmark, and stylised illustration is harder for us than photorealistic images. We would rather you knew that here than found out after paying.
Frequently asked questions
Related tools
Check your first image now
Create a free account and get 3 checks. Full breakdown across all 12 methods, no card required.