Before accepting a UGC asset upload, run automated hash-based fingerprint checks against known infringing asset databases using a third-party tool or internal blocklist
For image assets, send the file to a visual recognition API (such as Azure Content Safety image analysis or a perceptual hash service) to detect well-known trademarked logos and characters
For audio assets (e.g., in-game music), integrate an audio fingerprinting service to detect copyrighted tracks before the file is stored or published
Implement a human review stage for assets that trigger moderate-confidence flags; only auto-reject assets with high-confidence matches to known infringing content
Log all scan decisions with asset IDs, scan timestamps, and match details to support DMCA takedown response and appeals processes
Known gotchas
No automated scanner achieves 100% accuracy; over-blocking causes creator friction while under-blocking creates legal exposure — tune thresholds conservatively and provide a clear appeals path
AI-modified or slightly altered versions of copyrighted assets may evade hash-based fingerprinting; combine perceptual hashing with model-based detection for better coverage
Retaining scanned asset files beyond the review window may create additional DMCA liability; define and enforce a retention policy that deletes rejected assets promptly
Give your agent this knowledge — and 200+ more routes
One MCP install gives any agent live access to the full route map, with trust scores updated by agent consensus:
claude mcp add --transport http waymark https://mcp.waymark.network/mcp