SOTAVerified

Adversarial Defense

Competitions with currently unpublished results:

Papers

Showing 201–225 of 403 papers

TitleStatusHype
NOMARO: Defending against Adversarial Attacks by NOMA-Inspired Reconstruction OperationCode0
Segment and Complete: Defending Object Detectors against Adversarial Patch Attacks with Robust Patch DetectionCode1
Stochastic Local Winner-Takes-All Networks Enable Profound Adversarial RobustnessCode1
Class-Disentanglement and Applications in Adversarial Detection and Defense—0
Person Re-identification Method Based on Color Attack and Joint DefenceCode1
Rebuild and Ensemble: Exploring Defense Against Text Adversaries—0
Detection of Adversarial Examples in NLP: Benchmark and Baseline via Robust Density EstimationCode0
LSA: Modeling Aspect Sentiment Coherency via Local Sentiment AggregationCode0
Game Theory for Adversarial Attacks and DefensesCode0
Modeling Adversarial Noise for Adversarial Defense—0
Improving Adversarial Defense with Self-supervised Test-time Fine-tuning—0
Towards Achieving Adversarial Robustness Beyond Perceptual Limits—0
Two Souls in an Adversarial Image: Towards Universal Adversarial Example Detection using Multi-view InconsistencyCode0
Modeling Adversarial Noise for Adversarial TrainingCode0
TREATED:Towards Universal Defense against Textual Adversarial Attacks—0
Neural Ensemble Search via Bayesian Sampling—0
DropAttack: A Masked Weight Adversarial Training Method to Improve Generalization of Neural NetworksCode1
Delving into Deep Image Prior for Adversarial Defense: A Novel Reconstruction-based Defense Framework—0
AID-Purifier: A Light Auxiliary Network for Boosting Adversarial Defense—0
RAILS: A Robust Adversarial Immune-inspired Learning SystemCode1
NCIS: Neural Contextual Iterative Smoothing for Purifying Adversarial Perturbations—0
Helper-based Adversarial Training: Reducing Excessive Margin to Achieve a Better Accuracy vs. Robustness Trade-offCode1
Voting for the right answer: Adversarial defense for speaker verificationCode0
Adversarial Robustness via Fisher-Rao RegularizationCode0
Improving White-box Robustness of Pre-processing Defenses via Joint Adversarial Training—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1WRN-28-10Accuracy90.03—Unverified
2Diffusion ClassifierAccuracy89.85—Unverified
3Stochastic-LWTA/PGD/WideResNet-34-10Accuracy84.3—Unverified
4Ours (Stochastic-LWTA/PGD/WideResNet-34-5)Accuracy83.4—Unverified
5Ours (Stochastic-LWTA/PGD/WideResNet-34-1)Accuracy81.87—Unverified
6ResNet18 (TRADES-ANCRA/PGD-40)Accuracy81.7—Unverified
7Stochastic-LWTA/PGD/WideResNet-34-5Attack: AutoAttack81.22—Unverified
8PCL (against PGD, white box)Accuracy46.7—Unverified
#ModelMetricClaimedVerifiedStatus
1SAT-EfficientNet-L1Accuracy58.6—Unverified
2LLR-ResNet-152Accuracy47—Unverified
3ResNet-152 free-m=4Accuracy36—Unverified
4ResNet-101 free-m=4Accuracy34.3—Unverified
5ResNet-50 free-m=4Accuracy31.8—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet101Accuracy99.8—Unverified
2InceptionV3Accuracy98.6—Unverified
3Feature DenoisingAccuracy49.5—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet-152 DenoiseAccuracy42.8—Unverified
2ResNeXt-101 DenoiseAllAccuracy40.4—Unverified
3ResNet-152Accuracy39—Unverified
#ModelMetricClaimedVerifiedStatus
1Defense GANAccuracy0.85—Unverified
2PuVAEAccuracy0.81—Unverified
#ModelMetricClaimedVerifiedStatus
1Feature DenoisingAccuracy50.6—Unverified
#ModelMetricClaimedVerifiedStatus
1Auto Encoder-Block Switching defense with GradCAMAccuracy 88.54—Unverified