Loss-based
Model has lower loss on training data. Compute loss on candidate → below threshold → member. Simple but effective on undertrained models.
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Shadow models
Train shadow models with known membership. Learn classifier on loss patterns. Apply to target model. Shokri et al 2017.
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LLM-specific
Test perplexity on candidate. Compare to same-length random text. Lower relative → likely in training.