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Research map / Adversarial robustness and security / Attacks on text and graphs

CAP: Robust Point Cloud Classification via Semantic and Structural Modeling

CVPR 2023 · Adversarial robustness and security › Attacks on text and graphs · approach: Adversarial perturbations beyond images · 7 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add CAPPointNet · ModelNet40, 9 attacks (average)attack success rate: 70.2% -> 2.7%Helped
Add CAPDGCNN · ModelNet40, 9 attacks (average)attack success rate: 65.4% -> 3.5%Helped
Add CAPPointCNN · ModelNet40, 9 attacks (average)attack success rate: 16.7% -> 2.0%Helped
Add CAPPointNet · ModelNet40mean test accuracy: 86.2% -> 86.8%Helped
Add CAPDGCNN · ModelNet40mean test accuracy: 88.9% -> 90.1%Helped
Add CAPPointCNN · ModelNet40mean test accuracy: 86.8% -> 87.9%Helped
Add CAPPointNet · ShapeNetmean test accuracy: 78.6% -> 77.1%Hurt
Add CAPDGCNN · ShapeNetmean test accuracy: 80.5% -> 80.5%No clear effect
Add CAPPointCNN · ShapeNetmean test accuracy: 77.9% -> 79.4%Helped
Add CAPPointNet · ShapeNet, 6 attacks (average)attack success rate: 54.4% -> 1.5%Helped
Add CAPDGCNN · ShapeNet, 6 attacks (average)attack success rate: 49.2% -> 3.4%Helped
Add CAPPointCNN · ShapeNet, 6 attacks (average)attack success rate: 7.2% -> 1.8%Helped

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