Design an image copyright-violation detection system
Quick Overview
This question evaluates competency in designing scalable machine learning systems for image copyright detection, testing knowledge across computer vision, multimodal retrieval, similarity matching, robustness to adversarial edits, and handling partial matches like collages.
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Feb 12, 2026, 12:00 AM
Machine Learning Engineer
Onsite
ML System Design
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Design an ML system that detects whether a user-uploaded image violates copyright.
Requirements
- Input: an image uploaded by a user (optionally with text/caption and user/account metadata).
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Output: a decision such as
{allow, block, human_review}plus an explanation signal (e.g., matched copyrighted work ID, similarity score, region of match). - Must scale to large numbers of uploads and a large database of copyrighted/reference images.
Follow-up questions to address
- How would you fine-tune a pretrained vision(-language) model used for embeddings or classification?
- If an uploaded image is a 3×3 grid (collage) and only one tile is infringing, how do you detect that?
- How do you handle adversarial text overlays or other adversarial manipulations intended to evade detection?
- If the original work is copyrighted, does a photo of the work (e.g., taken by a camera from a screen/poster) count as infringement, and how should the system treat such near-duplicates?
Assume you can combine retrieval, classification, and human review, and you must justify metrics, thresholds, and latency/cost tradeoffs.
Solution
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