Methodology
Every AFIP determination is grounded in reproducible methodology. We publish our detection frameworks, document false positive rates, and submit our work to peer scrutiny. Claims without methodology are not forensic science — they are marketing.
AFIP's detection methodology operates on a principle of independent corroboration. Rather than relying on a single analytical method, our framework combines multiple independent approaches — each capable of producing a standalone determination. When independent methods converge on the same conclusion, confidence is high. When they diverge, the case is flagged for deeper forensic review.
For written content, the framework applies three layers: statistical distribution analysis (token entropy, perplexity variance, n-gram frequency), linguistic pattern recognition (syntactic complexity, discourse structure, stylistic consistency), and provenance fingerprinting (metadata analysis, platform-specific markers, editing history reconstruction).
For audio content, the framework applies spectral analysis across multiple frequency bands, vocal biomarker identification (14 distinct biomarker categories), and temporal coherence analysis. The multi-resolution approach has proven effective against neural voice synthesis systems.
For visual media, the framework applies GAN fingerprinting (frequency-domain model attribution), facial consistency analysis (boundary, temporal, and physiological plausibility), and compression artifact analysis (encoding-specific signature detection).
All AFIP methodologies undergo internal validation before publication. We maintain chain-of-custody documentation for all analyzed samples, publish our false positive and false negative rates, and disclose known limitations of each detection approach. Peer review is not optional — it is foundational to forensic credibility.