Membership Inference for Contrastive Pre-training Models with Text-only PII Queries
Contrastive pretraining models such as CLIP and CLAP, serve as the ubiquitous perceptual backbones for modern multimodal large models, yet their reliance on web-scale data raises growing concerns about memorizing Personally Identifiable Information (PII). Auditing such models via membership inference is challenging in practice: shadow-model MIAs are computationally prohibitive for large multimodal backbones, and existing multimodal auditing methods typically require querying the target with paired biometric inputs, thereby directly exposing sensitive biometric information to the target model. To bypass this critical limitation, we demonstrate a highly desirable capability for privacy audi...
| Authors | Ruoxi Cheng, Yizhong Ding, Jian Zhao et al. |
|---|---|
| Primary category | cs.CR |
| Published | 15 March 2026 |
| Last updated at source | 22 April 2026 |
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