DUNE optimizes perturbations in spatial and color domains with model ensembles to produce robust unlearnable examples that reduce test accuracy to 14.95%-50.82% under 7 defenses on CIFAR-10 and ImageNet, outperforming 12 prior methods.
Dolatabadi, Sarah M
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NTGA is the first clean-label generalization attack under black-box settings but is vulnerable to adversarial training and image transformations, with newer attacks outperforming it.
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Dual-branch Robust Unlearnable Examples
DUNE optimizes perturbations in spatial and color domains with model ensembles to produce robust unlearnable examples that reduce test accuracy to 14.95%-50.82% under 7 defenses on CIFAR-10 and ImageNet, outperforming 12 prior methods.
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SoK: A Comprehensive Analysis of the Current Status of Neural Tangent Generalization Attacks with Research Directions
NTGA is the first clean-label generalization attack under black-box settings but is vulnerable to adversarial training and image transformations, with newer attacks outperforming it.