Adversarial perturbations reliably fabricate membership signals in vision-model MIAs, separated by a gradient-norm collapse trajectory that enables robust detection and inference.
On the importance of difficulty calibra- tion in membership inference attacks
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Finetuning open LMs on ChatGPT outputs creates models that mimic style and fool human raters but fail to close the performance gap to proprietary systems on tasks not well-represented in the imitation data.
Advanced generalization techniques such as augmentation and early stopping can reduce membership inference attack success rates by up to 100 times, confirmed across more than 1,000 models.
citing papers explorer
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A Unified Perspective on Adversarial Membership Manipulation in Vision Models
Adversarial perturbations reliably fabricate membership signals in vision-model MIAs, separated by a gradient-norm collapse trajectory that enables robust detection and inference.
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The False Promise of Imitating Proprietary LLMs
Finetuning open LMs on ChatGPT outputs creates models that mimic style and fool human raters but fail to close the performance gap to proprietary systems on tasks not well-represented in the imitation data.
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Generalization and Membership Inference Attack a Practical Perspective
Advanced generalization techniques such as augmentation and early stopping can reduce membership inference attack success rates by up to 100 times, confirmed across more than 1,000 models.