Description
Implement AI-powered content moderation to automatically detect and flag inappropriate, offensive, or harmful content in real-time as users draw. Use vision models and text analysis to maintain a safe, collaborative environment while respecting user privacy.
Current workflow
No automated content moderation; relies on user reports and manual review.
Proposed solution
Integrate vision-based ML models (e.g., OpenAI Moderation API, Google Cloud Vision API, or self-hosted models) to analyze canvas content in real-time. Flag or blur inappropriate content, notify moderators, and provide user-facing warnings.
Technical requirements
Files to create:
backend/services/content_moderation_service.pybackend/routes/moderation.pyfrontend/src/components/Moderation/ContentWarning.jsxfrontend/src/components/Moderation/ModerationPanel.jsxbackend/services/vision_analysis_service.py
Files to modify:
submit_room_line.pysocketio_handlers.pyCanvas.jsconfig.py
Backend:
db.py (moderation_logs collection)middleware/auth.py (moderator role checks)
Skills
Computer vision, ML model integration, Python async processing, React UI, content policy design
Key features
- Real-time stroke analysis on submission
- Configurable sensitivity levels (strict/moderate/permissive)
- Content warning overlays for flagged areas
- Moderator dashboard with review queue
- Appeal system for false positives
- Privacy-preserving local analysis option
- Automated blur/redaction for severe violations
Challenges
Balancing false positives vs false negatives, processing latency, privacy concerns, model bias, cost of API calls, handling edge cases in abstract art
Getting started
Implement moderation service wrapper, add pre-submission hooks, create warning UI, add moderator endpoints
Tests
Unit: known-bad content correctly flagged
Integration: flagged stroke → moderator notification
UI: content warning displays and can be dismissed by moderator
Performance: moderation check < 200ms
Privacy: no canvas data sent externally if local mode enabled
Resources
OpenAI Moderation API, TensorFlow.js NSFW model, Google Cloud Vision, moderation best practices
Description
Implement AI-powered content moderation to automatically detect and flag inappropriate, offensive, or harmful content in real-time as users draw. Use vision models and text analysis to maintain a safe, collaborative environment while respecting user privacy.
Current workflow
No automated content moderation; relies on user reports and manual review.
Proposed solution
Integrate vision-based ML models (e.g., OpenAI Moderation API, Google Cloud Vision API, or self-hosted models) to analyze canvas content in real-time. Flag or blur inappropriate content, notify moderators, and provide user-facing warnings.
Technical requirements
Files to create:
backend/services/content_moderation_service.pybackend/routes/moderation.pyfrontend/src/components/Moderation/ContentWarning.jsxfrontend/src/components/Moderation/ModerationPanel.jsxbackend/services/vision_analysis_service.pyFiles to modify:
submit_room_line.pysocketio_handlers.pyCanvas.jsconfig.pyBackend:
db.py(moderation_logs collection)middleware/auth.py(moderator role checks)Skills
Computer vision, ML model integration, Python async processing, React UI, content policy design
Key features
Challenges
Balancing false positives vs false negatives, processing latency, privacy concerns, model bias, cost of API calls, handling edge cases in abstract art
Getting started
Implement moderation service wrapper, add pre-submission hooks, create warning UI, add moderator endpoints
Tests
Unit: known-bad content correctly flagged
Integration: flagged stroke → moderator notification
UI: content warning displays and can be dismissed by moderator
Performance: moderation check < 200ms
Privacy: no canvas data sent externally if local mode enabled
Resources
OpenAI Moderation API, TensorFlow.js NSFW model, Google Cloud Vision, moderation best practices