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Dual Manifold Adversarial Robustness: Defense against Lp and non-Lp Adversarial Attacks

NeurIPS 2020 · Adversarial robustness and security › Certified and empirical robustness · approach: Attack evaluation and defense critique · 25 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add on-manifold adversarial training term to standard AT (ablation)AT [PGD-5] · OM-ImageNet test OM-PGD-50accuracy: 7.23 -> 20.53Helped
add on-manifold adversarial training term to standard AT (ablation)AT [PGD-5] · OM-ImageNet test cleanaccuracy: 73.31 -> 77.96Helped
add on-manifold term to standard AT (ablation)AT [PGD-5] · OM-ImageNet test PGD-50accuracy: 38.88 -> 37.86No clear effect
add standard (off-manifold) AT term to on-manifold AT (ablation)OM-AT [OM-PGD-5] · OM-ImageNet test PGD-50accuracy: 0.25 -> 37.86Helped
add standard AT term to on-manifold AT (ablation)OM-AT [OM-PGD-5] · OM-ImageNet test OM-PGD-50accuracy: 27.53 -> 20.53Hurt
add on-manifold term to standard AT (ablation)AT [PGD-5] · OM-ImageNet test Fog attackaccuracy: 19.76 -> 31.78Helped
add on-manifold term to standard AT (ablation)AT [PGD-5] · OM-ImageNet test L1 attackaccuracy: 21.21 -> 29.68Helped
add on-manifold term to standard AT (ablation)AT [PGD-5] · OM-ImageNet test Gabor attackaccuracy: 50.43 -> 51.61No clear effect
add on-manifold term to TRADES (ablation)TRADES · OM-ImageNet test OM-PGD-50accuracy: 8.92 -> 26.82Helped
add on-manifold term to TRADES (ablation)TRADES · OM-ImageNet test PGD-50accuracy: 46.06 -> 42.57Hurt
add on-manifold term to TRADES (ablation)TRADES · OM-ImageNet test cleanaccuracy: 69.88 -> 73.17Helped
add on-manifold term to standard AT (ablation)AT [PGD-5] · natural out-of-manifold images, L1 attackaccuracy: 21.21 -> 29.68Helped

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