The science of
seeing through

We present a detection architecture that combines provenance metadata, statistical forensics, semantic vision analysis, and specialized classifiers into a unified attribution pipeline. By treating each signal source as an orthogonal feature dimension and applying confidence-weighted cascading, the system achieves high accuracy while minimizing inference cost. Early experiments suggest the approach outperforms single-detector baselines by significant margins on cross-generator benchmarks.