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Research map / Adversarial robustness and security / Certified and empirical robustness

Subspace Adversarial Training

CVPR 2022 · Adversarial robustness and security › Certified and empirical robustness · approach: Adversarial training recipes · 65 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Constrain single-step GAT to the extracted subspaceGAT on CIFAR-10 · CIFAR-10, PGD-50, best robust accuracyrobust accuracy: 50.03 -> 51.15Helped
Constrain single-step GradAlign to the extracted subspaceGradAlign on CIFAR-100 · CIFAR-100, PGD-50, best robust accuracyrobust accuracy: 23.80 -> 25.69Helped
Constrain PGD-10 AT to the extracted subspacePGD-10 AT on CIFAR-10 · CIFAR-10, PGD-20, best robust accuracy, l-infinity epsilon=8/255robust accuracy: 51.09 -> 52.79Helped
Constrain PGD-10 AT to the extracted subspacePGD-10 AT on CIFAR-10 · CIFAR-10, Auto-Attack, best robust accuracy, l-infinity epsilon=8/255robust accuracy: 47.29 -> 48.37Helped
Increase Fast Sub-AT training radius from 8/255 to 12/255 (ablation)Fast Sub-AT on CIFAR-10 · CIFAR-10, PGD-50, best robust accuracyrobust accuracy: 48.22 -> 50.38Helped
Increase Fast Sub-AT training radius from 8/255 to 16/255 (ablation)Fast Sub-AT on CIFAR-10 · CIFAR-10, PGD-50, best robust accuracyrobust accuracy: 48.22 -> 51.46Helped
Increase Fast Sub-AT training radius from 8/255 to 16/255 (ablation)Fast Sub-AT in the PGD-10 AT subspace on CIFAR-10 · CIFAR-10, PGD-50, final robust accuracyrobust accuracy: 52.01 -> 54.11Helped
Increase Fast Sub-AT training radius from 8/255 to 16/255 (ablation)Fast Sub-AT on Tiny-ImageNet · Tiny-ImageNet, PGD-20, best robust accuracyrobust accuracy: 16.75 -> 18.79Helped
Vary the subspace dimension from 50 to 100 (ablation)Fast Sub-AT · CIFAR-10, PGD-20, best robust accuracyrobust accuracy: <1%No clear effect
Replace the full PGD-10 AT training process with Sub-AT optimizationPGD-10 AT on Tiny-ImageNet · Tiny-ImageNet, PGD-20, best robust accuracyrobust accuracy: 19.84 -> 20.52Helped

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