Adversarial perturbations reliably fabricate membership signals in vision-model MIAs, separated by a gradient-norm collapse trajectory that enables robust detection and inference.
Cinic-10 is not imagenet or cifar-10
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
citation-role summary
background 1
dataset 1
citation-polarity summary
years
2026 4representative citing papers
HARMONY mitigates representation skew in heterogeneous hybrid split federated learning via meta-learning to simulate diverse extractors and server-side contrastive learning to align features, delivering up to 43% accuracy gains.
ArmSSL is a black-box verifiable and adversarially robust watermarking framework for SSL pre-trained encoders using paired discrepancy enlargement, latent entanglement, distribution alignment, and reference-guided tuning.
citing papers explorer
-
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.
-
HARMONY: Bridging the Personalization-Generalization Gap by Mitigating Representation Skew in Heterogeneous Split Federated Learning
HARMONY mitigates representation skew in heterogeneous hybrid split federated learning via meta-learning to simulate diverse extractors and server-side contrastive learning to align features, delivering up to 43% accuracy gains.
-
ArmSSL: Adversarial Robust Black-Box Watermarking for Self-Supervised Learning Pre-trained Encoders
ArmSSL is a black-box verifiable and adversarially robust watermarking framework for SSL pre-trained encoders using paired discrepancy enlargement, latent entanglement, distribution alignment, and reference-guided tuning.
- Revisiting Privacy Leakage in Machine Unlearning: Membership Inference Beyond the Forgotten Set