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

DANCE: Enhancing saliency maps using decoys

ICML 2021 · Adversarial robustness and security › Certified and empirical robustness · approach: Decision-boundary geometry of robustness · 1 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add DANCE decoys and range aggregation to the saliency methodVanilla gradient, integrated gradient, and SmoothGrad · ImageNetfidelityHelped
Add DANCE decoys and range aggregation to the saliency methodVanilla gradient, integrated gradient, and SmoothGrad · SSTfidelityHelped
Add DANCE decoys and range aggregation to the saliency methodSaliency methods · ImageNet adversarial attacks: top-k, target, and mass-centersensitivityHelped
Replace optimized decoy generation with constant perturbation (ablation)DANCE · ImageNetfidelityHurt
Replace optimized decoy generation with Gaussian noise perturbation (ablation)DANCE · ImageNetfidelityHurt
Replace range aggregation with mean aggregation (ablation)DANCE · ImageNetfidelityHurt
Replace optimized decoy generation with constant perturbation (ablation)DANCE · SSTfidelityHurt
Replace optimized decoy generation with noise perturbation (ablation)DANCE · SSTfidelityHurt
Replace range aggregation with mean aggregation (ablation)DANCE · SSTfidelityHurt
Vary the number of decoys (ablation)DANCE · ImageNetfidelityNo clear effect
Vary patch size, target network layer, or initial Lagrange multiplier (ablation)DANCE · ImageNetfidelityNo clear effect

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