Research map / Adversarial robustness and security / Certified and empirical robustness
Subspace Adversarial Training
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Constrain single-step GAT to the extracted subspace | GAT on CIFAR-10 · CIFAR-10, PGD-50, best robust accuracy | robust accuracy: 50.03 -> 51.15 | Helped |
| Constrain single-step GradAlign to the extracted subspace | GradAlign on CIFAR-100 · CIFAR-100, PGD-50, best robust accuracy | robust accuracy: 23.80 -> 25.69 | Helped |
| Constrain PGD-10 AT to the extracted subspace | PGD-10 AT on CIFAR-10 · CIFAR-10, PGD-20, best robust accuracy, l-infinity epsilon=8/255 | robust accuracy: 51.09 -> 52.79 | Helped |
| Constrain PGD-10 AT to the extracted subspace | PGD-10 AT on CIFAR-10 · CIFAR-10, Auto-Attack, best robust accuracy, l-infinity epsilon=8/255 | robust accuracy: 47.29 -> 48.37 | Helped |
| 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 accuracy | robust accuracy: 48.22 -> 50.38 | Helped |
| 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 accuracy | robust accuracy: 48.22 -> 51.46 | Helped |
| 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 accuracy | robust accuracy: 52.01 -> 54.11 | Helped |
| 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 accuracy | robust accuracy: 16.75 -> 18.79 | Helped |
| Vary the subspace dimension from 50 to 100 (ablation) | Fast Sub-AT · CIFAR-10, PGD-20, best robust accuracy | robust accuracy: <1% | No clear effect |
| Replace the full PGD-10 AT training process with Sub-AT optimization | PGD-10 AT on Tiny-ImageNet · Tiny-ImageNet, PGD-20, best robust accuracy | robust accuracy: 19.84 -> 20.52 | Helped |
Closest papers by meaning
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- Enhancing the Adversarial Robustness via Manifold Projection (AAAI 2025 · similarity 0.87)
- Efficient local linearity regularization to overcome catastrophic overfitting (ICLR 2024 · similarity 0.87)
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