Technology
Principles
The detection methodology and AI principles that make FalsiFind's results defensible in regulatory and legal contexts.
Core Technology Principles
Three foundational principles that shape every technical decision we make.
Multi-Model Detection
No single model can detect all synthetic media. We ensemble multiple detection approaches, each specialized for different generation methods, and combine their signals for robust classification.
Cryptographic Evidence
Every detection generates a cryptographically signed evidence bundle using RSA-PSS signatures. Timestamps follow RFC 3161 standards. Evidence is tamper-evident and designed for regulatory and legal scrutiny.
Minimized Media Retention
Customer media is held for 30 days or less, used only to train FalsiFind's detection models, and deleted when training completes. Customers who require it can use zero-storage processing, where media is analyzed in memory and discarded immediately.
Technical Approach
How we approach detection challenges with practical, defensible methodology.
Generation-Agnostic Detection
Our models identify artifacts of synthetic generation rather than fingerprinting specific tools. This means detection works against new generation methods, not just the ones we've seen before.
Continuous Model Updates
Detection models are updated regularly as new generation techniques emerge. Updates deploy without service interruption and without requiring customer code changes.
Confidence Calibration
Our confidence scores are calibrated to reflect true probabilities. A 90% confidence score means approximately 90% of similar cases are correctly classified, not an arbitrary threshold.
What We Keep Proprietary
We're transparent about our principles and approach, but we don't disclose implementation details that could help adversaries evade detection.