MAPE combines a channel-attention U-Net (SAPE) trained on multi-model adversarial examples scheduled by PPSA to eliminate perturbations, reporting over 95.1% average defense on CIFAR-10 and 71.5% on Mini-ImageNet against black-box transferable attacks.
Query-efficient black-box adversarial attack with customized iteration and sampling
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
RESSAP creates a model-agnostic ensemble of classifiers using resilience-guided feature selection, random subset inference, and noise augmentation to boost robustness to evasion attacks while preserving clean accuracy.
citing papers explorer
-
Robust Ensemble of Selectively Strengthened and Augmented Predictors
RESSAP creates a model-agnostic ensemble of classifiers using resilience-guided feature selection, random subset inference, and noise augmentation to boost robustness to evasion attacks while preserving clean accuracy.