The base case felt manageable: perceptual hashing, embedding similarity, maybe a classifier on top.
Start by clarifying requirements and constraints, then propose a multi-stage system that combines detection, localization, and classification. Address each edge case explicitly with tailored techniques, and discuss trade-offs between accuracy, latency, and scalability.
Pro tip: Emphasize the importance of a human-in-the-loop for borderline cases and the need for a feedback mechanism to continuously improve the model, as copyright infringement is nuanced and context-dependent.
Ask about scale, latency requirements, definition of infringement, and legal considerations. Understand what constitutes a violation and the acceptable false positive/negative rates.
Propose a two-stage approach: first, a fast candidate retrieval using perceptual hashing or embeddings to find potential matches; second, a more precise verification using deep learning models for classification and localization.
For collages, use object detection or segmentation to identify regions and match each part. For adversarial text overlays, employ robust feature extraction and possibly OCR to detect and ignore overlays. For near-duplicates, use metric learning to capture semantic similarity.
Discuss indexing strategies (e.g., FAISS) for efficient similarity search, model quantization for latency, and the trade-off between precision and recall. Consider distributed processing for large-scale uploads.
Define metrics (e.g., precision, recall, mAP) and propose an evaluation framework with a labeled dataset. Suggest A/B testing and continuous learning from user reports and appeals.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.