Reproducible, explainable, and traceable analysis across face manipulation, AI-generated content, and synthetic voice — backed by reproducible, version-stamped chain-of-custody bundles and a per-use-case error rate we publish.
Identifies gen-AI face manipulation by analysing visual artefacts, biometric inconsistencies, and generative patterns. Four attack categories. Minimum three sub-signals per finding. Every result linked to a plain-language reason code.