Content moderation

At platform scale, detection runs as an automated scoring step in the upload pipeline: every image and video is analyzed for generation artifacts, face swaps, and manipulation before publication, and the score feeds moderation rules as spam and abuse signals already do. Human review is reserved for the contested middle. ScamAI's REST API is built for this pattern, with real-time results at upload, batch endpoints for library rescans, and evidence attached to each result for moderator review and transparency reporting.

Real-time detection at upload volume is an architecture question: the check must return a result within the latency budget of the publish flow and scale with traffic. ScamAI's API is designed for both modes — synchronous scoring for the upload path and batch processing for backfills and rescans — so platforms do not choose between coverage and speed. The practical pattern is scoring everything at ingest, auto-actioning only at high confidence, and letting the moderation queue absorb the borderline cases.

Most platforms are converging on tiered enforcement rather than blanket removal: benign synthetic content stays up with a label, synthetic depictions of real people face restrictions, and deceptive deepfakes — fabricated statements, non-consensual imagery, fraud content — are removed. Every tier depends on knowing the content is synthetic, which self-disclosure alone cannot provide. Automated detection supplies that knowledge at scale, and results with evidence let platforms defend both the label and the takedown in appeals and transparency reports.