SOTAVerified

Adversarial Attack

An Adversarial Attack is a technique to find a perturbation that changes the prediction of a machine learning model. The perturbation can be very small and imperceptible to human eyes.

Source: Recurrent Attention Model with Log-Polar Mapping is Robust against Adversarial Attacks

Papers

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Showing 1111–1120 of 1808 papers

TitleStatusHype
Multi-Expert Adversarial Attack Detection in Person Re-identification Using Context Inconsistency—0
Multi-granular Adversarial Attacks against Black-box Neural Ranking Models—0
Adversarial Sampling for Fairness Testing in Deep Neural Network—0
Towards Building a Robust Toxicity Predictor—0
Multi-head Uncertainty Inference for Adversarial Attack Detection—0
Towards Calibration Enhanced Network by Inverse Adversarial Attack—0
Multi-objective Evolutionary Search of Variable-length Composite Semantic Perturbations—0
Multi-step domain adaptation by adversarial attack to H ΔH-divergence—0
Multi-Task Adversarial Attack—0
Adversarial Robustness through Dynamic Ensemble Learning—0
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All datasetsCIFAR-10CIFAR-100

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Xu et al.Attack: PGD2078.68—Unverified
23-ensemble of multi-resolution self-ensemblesAttack: AutoAttack78.13—Unverified
3TRADES-ANCRA/ResNet18Attack: AutoAttack59.7—Unverified
4AdvTraining [madry2018]Attack: PGD2048.44—Unverified
5TRADES [zhang2019b]Attack: PGD2045.9—Unverified
6XU-NetRobust Accuracy1—Unverified
#ModelMetricClaimedVerifiedStatus
13-ensemble of multi-resolution self-ensemblesAttack: AutoAttack51.28—Unverified
2multi-resolution self-ensemblesAttack: AutoAttack47.85—Unverified