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Friendly Noise against Adversarial Noise: A Powerful Defense against Data Poisoning Attack

NeurIPS 2022 · Adversarial robustness and security › Attacks on text and graphs · approach: Clean-label data poisoning · 4 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add friendly and varying Bernoulli noiseResNet-18 trained on CIFAR-10 · Gradient Matching (ξ=16)Poison Acc: 75.00 -> 0.00Helped
Add friendly and varying Bernoulli noiseResNet-18 trained on CIFAR-10 · Gradient Matching (ξ=16)Test Acc: 93.50 -> 91.52Hurt
Combine friendly noise with varying random noise rather than using friendly noise alone (ablation)FrieNDs on ResNet-18 · Gradient Matching (ξ=16)Poison Acc: 10.00 -> 0.00Helped
Combine friendly noise with smaller Gaussian noise rather than using Gaussian noise alone (ablation)FrieNDs on ResNet-18 · Gradient Matching (ξ=16)Test Acc: 89.46 -> 91.50Helped
Use friendly noise without varying random noise (ablation)FrieNDs on ResNet-18 · Gradient Matching (ξ=16)Poison Acc: 0.00 -> 10.00Hurt
Replace Bernoulli random noise with uniform noise (ablation)FrieNDs on ResNet-18 · Gradient Matching (ξ=16)Poison Acc: 0.00 -> 5.00Hurt
Replace the L2 friendly-noise objective with L1 (ablation)FrieNDs-B on ResNet-18 · Gradient Matching (ξ=16)Test Acc: 91.52% -> 91.50%No clear effect
Replace the L2 friendly-noise objective with L∞ (ablation)FrieNDs-B on ResNet-18 · Gradient Matching (ξ=16)Test Acc: 91.52% -> 91.37%No clear effect
Use FrieNDs-B against Sleeper AgentSleeper Agent defense on 24 datasets · Sleeper AgentPoison Acc: 83.48% -> 21.52%Helped
Use FrieNDs-B against Sleeper AgentSleeper Agent defense on 24 datasets · Sleeper AgentTest Acc: 91.56% -> 89.76%Hurt
Adapt Sleeper Agent poison generation to Bernoulli noiseFrieNDs-B · Adaptive Sleeper Agent attackPoison Acc: 21.52 ± 8.39 -> 31.05 ± 9.45Mixed
Adapt Sleeper Agent poison generation to FrieNDs-BFrieNDs-B · Adaptive Sleeper Agent attackPoison Acc: 21.52 ± 8.39 -> 30.50 ± 9.40Mixed

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