MADreMIA amplifies membership inference signals by showing that memorized samples maintain higher coherence and slower degradation in chained regeneration trajectories than non-members.
Dataset inference: Ownership resolution in machine learning
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
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UNVERDICTED 3representative citing papers
Landseer offers a containerized modular system to integrate and evaluate combinations of machine learning defenses, with an initial analysis of 35 defenses highlighting replicability challenges.
A2-DIDM uses accumulators and ZK proofs on blockchain to verify DNN model identity from weight checkpoint sequences while protecting data and function privacy.
citing papers explorer
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Amplifying Membership Signal Through Chained Regeneration
MADreMIA amplifies membership inference signals by showing that memorized samples maintain higher coherence and slower degradation in chained regeneration trajectories than non-members.
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Landseer: Exploring the Machine Learning Defense Landscape
Landseer offers a containerized modular system to integrate and evaluate combinations of machine learning defenses, with an initial analysis of 35 defenses highlighting replicability challenges.
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A2-DIDM: Privacy-preserving Accumulator-enabled Auditing for Distributed Identity of DNN Model
A2-DIDM uses accumulators and ZK proofs on blockchain to verify DNN model identity from weight checkpoint sequences while protecting data and function privacy.