This research presents a more efficient methodology to detect when AI models accidentally memorize their training data, with significant implications for data privacy and security.

• Introduces a faster, more efficient approach to measure the "déjà vu" effect in AI models
• Reduces computational resources needed by eliminating the requirement to train multiple models
• Enables proactive identification of potential data leakage vulnerabilities
• Provides practical tools for security audits of representation learning models

For security professionals and AI governance teams, this research offers critical capabilities to identify when models might inadvertently expose sensitive training data, supporting compliance with privacy regulations and protecting against data extraction attacks.

Measuring Déjà vu Memorization Efficiently