I spent the first chunk of time on the standard stuff, upload pipeline, storage, CDN, and kind of forgot the whole point was the authenticity angle.
Start by clarifying the product goals and constraints, then outline the core system architecture for video upload, storage, and delivery. Integrate authenticity verification as a cross-cutting concern, discussing detection mechanisms, trade-offs, and user experience. Conclude with metrics and iteration plan.
Pro tip: Emphasize a layered verification approach that balances automation with human review, and proactively address adversarial evolution to show strategic thinking.
Ask questions to understand target users, scale, content types, and business objectives. Define what 'authentic' means in this context (e.g., original, unmanipulated, not misleading).
Sketch the core components: upload service, transcoding pipeline, storage (object store + CDN), metadata database, and delivery APIs. Ensure scalability and fault tolerance.
Propose a multi-layered approach: cryptographic hashing for duplicate detection, perceptual hashing for near-duplicates, AI models for deepfake detection, metadata analysis, and user reporting. Discuss real-time vs. post-upload checks.
Analyze trade-offs: false positives vs. false negatives, latency vs. accuracy, cost vs. coverage. Decide on user experience for flagged content (e.g., warning labels, removal, appeals).
Define success metrics (e.g., detection rate, false positive rate, user trust). Outline a feedback loop for model improvement and adaptation to new manipulation techniques.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.