Yeah but does it actually work, though? There have been a lot of online tools claiming to be "AI detectors" and they all seem pretty unreliable. Can you talk us through what you look for, the most common failure modes and (at suitably high level) how you dealt with those?
In terms of how our technology works, our research team has trained multiple detection models to look for specific visual and audio artifacts that the major generative models leave behind. These artifacts aren't perceptible to the human eye / ear, but they are actually very detectable to computer vision and audio models.
Each of these expert models gets combined into an ensemble system that weighs all the individual model outputs to reach a final conclusion.
We've got a rigorous process of collecting data from new generators, benchmarking them, and retraining our models when necessary. Often retrains aren't needed though, since our accuracy seems to transfer well across a given deepfake technique. So even if new diffusion or autoregressive models come out, for example, the artifacts tend to be similar and are still caught by our models.
I will say that our models are most heavily benchmarked on convincing audio/video/image impersonations of humans. While we can return results for items outside that scope, we've tended to focus training and benchmarking on human impersonations since that's typically the most dangerous risk for businesses.
So that's a caveat to keep in mind if you decide to try out our Developer Free Plan.
