Membership inference against contrastive encoders is more accurate for larger frameworks and backbones, and a lightweight likelihood attack based on feature-vector p-norms matches or beats prior attacks with fewer queries.
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When Better Features Mean Greater Risks: The Performance-Privacy Trade-Off in Contrastive Learning
Membership inference against contrastive encoders is more accurate for larger frameworks and backbones, and a lightweight likelihood attack based on feature-vector p-norms matches or beats prior attacks with fewer queries.