Identifies sensitivity as the source of both discriminability and vulnerability in FC classifiers versus robustness in l2 classifiers, and introduces HPM prototype fusion plus MSA evaluation to improve adversarial robustness.
Adversarial robustness limits via scaling-law and human-alignment studies
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
representative citing papers
A provable adversarial noise amplification theorem under sufficient conditions enables a custom-trained detector that identifies adversarial examples at inference time using enhanced layer-wise noise signals.
The FastAT Benchmark standardizes evaluation of over twenty fast adversarial training methods under unified conditions, showing that well-designed single-step approaches can match or exceed PGD-AT robustness at lower training cost on CIFAR-10, CIFAR-100, and Tiny-ImageNet.
SAAD adaptively weights adversarial training samples by their transferability to the teacher, yielding higher AutoAttack robustness than prior distillation methods on CIFAR and Tiny-ImageNet without extra compute.
citing papers explorer
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Sensitivity as a Double-Edged Sword: A Trade-off Between Discriminability and Adversarial Robustness
Identifies sensitivity as the source of both discriminability and vulnerability in FC classifiers versus robustness in l2 classifiers, and introduces HPM prototype fusion plus MSA evaluation to improve adversarial robustness.
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Detecting Adversarial Data via Provable Adversarial Noise Amplification
A provable adversarial noise amplification theorem under sufficient conditions enables a custom-trained detector that identifies adversarial examples at inference time using enhanced layer-wise noise signals.
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FastAT Benchmark: A Comprehensive Framework for Fair Evaluation of Fast Adversarial Training Methods
The FastAT Benchmark standardizes evaluation of over twenty fast adversarial training methods under unified conditions, showing that well-designed single-step approaches can match or exceed PGD-AT robustness at lower training cost on CIFAR-10, CIFAR-100, and Tiny-ImageNet.
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Sample-wise Adaptive Weighting for Transfer Consistency in Adversarial Distillation
SAAD adaptively weights adversarial training samples by their transferability to the teacher, yielding higher AutoAttack robustness than prior distillation methods on CIFAR and Tiny-ImageNet without extra compute.