Every vendor claims high accuracy. Almost none of the claims mean the same thing. This is the single most confusing part of buying detection, so let me unpack it slowly.
The core problem is that there is no standard test. Each vendor picks its own datasets, which means the same tool can look brilliant on one benchmark and ordinary on another. Comparing two vendors' self-reported numbers is like comparing two restaurants' self-awarded stars. Technically numbers. Actually fiction.
Then the metric maze. Accuracy, precision, recall, AUC, equal error rate. Vendors report whichever flatters the product that quarter. Here is a trap I see constantly: great accuracy on a dataset that is mostly real videos tells you almost nothing about catching fakes. Always ask which metric, on what data, against what baseline. Get it in writing if you can.
Generalization is the real test and the rarest report. A detector trained and tested on the same generation methods will look fantastic and fail on next month's models. Honest vendors talk about cross-dataset performance. The others change the subject, and you should notice that.
This is why a deepfake detection accuracy database beats any single vendor whitepaper. Claims collected side by side, with test conditions attached, make the gaps visible. You stop comparing percentages and start comparing evidence. Night and day.
So how do you actually compare? Independent evaluations first. Test conditions behind every claim second. A pilot on your own data third, because your data is the only benchmark that matters for your decision. Everything else is a hint.
The best deepfake detection software 2026 roundups can seed a shortlist, fine. And a deepfake detection tool comparison database search will surface dozens of options. But the only ones worth your time are the ones whose accuracy claims arrive with context: dataset, metric, date, independence.
The database at deepfakedetect.fyi collects each tool's stated accuracy claims alongside modality and test context, so you see what sits behind the percentages. Every deepfake detection tool gets the same fields. That is the whole point.