Design an image/video near-duplicate detection system

Q: Design an image/video near-duplicate detection system

This question evaluates competency in ML system design and large-scale multimedia retrieval, focusing on perceptual fingerprinting versus embedding strategies, scalable indexing and nearest-neighbor retrieval, and robustness to resizing, re-encoding, watermarks, minor edits, and adversarial manipulations.

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Question

Question

Design a system to detect near-duplicate images/videos (e.g., reuploads, minor edits, different encodes) at large scale.

Requirements

Support both images and videos.
Robust to resizing, cropping, re-encoding, watermarks, small edits.
High throughput ingestion; low-latency query for takedown/merge/dedup.
Handle billions of media items.

Deliverables

Fingerprinting approach (perceptual hashing vs embeddings).
Indexing and retrieval architecture.
Thresholding, evaluation, and operational concerns (false positives, adversarial behavior).

Design an image/video near-duplicate detection system

Question

Requirements

Deliverables

Solution

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Design an image/video near-duplicate detection system

Overview

Question

Requirements

Deliverables

Solution

Comments (0)