Adversarial content classification: lessons from operating at web scale
What a decade of operating content-safety systems on a 45M+ monthly-active-user surface teaches about adversarial classification. Covers signal inversion, encoding tricks, cross-modal evasion, context collapse, and the failure modes of single-modality classifiers under sustained adversarial pressure. Frames the design principles AEGIS inherits from this experience.
Abstract
What a decade of operating content-safety systems on a 45M+ monthly-active-user surface teaches about adversarial classification. Covers signal inversion, encoding tricks, cross-modal evasion, context collapse, and the failure modes of single-modality classifiers under sustained adversarial pressure. Frames the design principles AEGIS inherits from this experience.
Status
Research note · full PDF pending. This page is the canonical abstract for now. The complete paper publishes once we finalize external review and distribution; this page links to it on the same URL when ready. Subscribe for release alerts via contact · research interest.