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Isometric 3D Adversarial Examples in the Physical World

NeurIPS 2022 · Adversarial robustness and security › Attacks on text and graphs · approach: Adversarial perturbations beyond images · 17 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
constrain adversarial mesh to be epsilon-isometric via Gaussian curvature regularization (epsilon-ISO) vs Mesh AttackPointNet · ModelNet40attack success rate (%): 88.10 -> 98.45Helped
use epsilon-ISO instead of Mesh Attack under simple defenses SRSPointNet · SRS defenseattack success rate (%): 87.38 -> 79.85Hurt
use epsilon-ISO instead of Mesh Attack under IF-DefencePointNet · IF-Defenceattack success rate (%): 49.06 -> 60.51Helped
replace EOT with MaxOT (worst-case transformation)epsilon-ISO · ModelNet40 rotationattack success rate (%): 76.20 -> 82.50 (PointNet)Helped
use Bayesian optimization for initial transformation instead of random (ablation)MaxOT · ModelNet40 PointNetattack success rate (%): 80.59 -> 82.50Helped
MaxOT instead of EOT in physical worldPointNet · 3D-printed and scannedattack success rate (%): 72.36 -> 80.12Helped
increase lambda2 from 0.2 to 1 (ablation)epsilon-ISO · ModelNet40 PointNetattack success rate (%): 98.45 -> 94.22Hurt
increase lambda3 from 0.8 to 16 (ablation)epsilon-ISO · ModelNet40 PointNetattack success rate (%): 98.45 -> 63.01Hurt
replace Gaussian curvature regularizer with mean curvature (ablation)epsilon-ISO · ModelNet40naturalnessHurt
epsilon-ISO instead of GeoA3 for black-box transfer from PointNet to PointNet++PointNet · transfer attackattack success rate (%): 11.5 -> 47.6Helped

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