Adversarial perturbations reliably fabricate membership signals in vision-model MIAs, separated by a gradient-norm collapse trajectory that enables robust detection and inference.
Ml privacy meter: Aiding regulatory compliance by quantifying the privacy risks of machine learning
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Concatenated MIA score evaluation is uncalibrated across per-sample FPRs and the efficient LiRA has finite population bias; a post-processing calibration is proposed.
FML-Bench shows a simple greedy hill-climber nearly matches tree search on dense-opportunity tasks while an adaptive agent that broadens search on stagnation outperforms six baselines across 18 tasks.
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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On Reliability of Efficient Membership Inference Vulnerability Evaluation
Concatenated MIA score evaluation is uncalibrated across per-sample FPRs and the efficient LiRA has finite population bias; a post-processing calibration is proposed.
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FML-bench: A Controlled Study of AI Research Agent Strategies from the Perspective of Search Dynamics
FML-Bench shows a simple greedy hill-climber nearly matches tree search on dense-opportunity tasks while an adaptive agent that broadens search on stagnation outperforms six baselines across 18 tasks.