SOTAVerified

Adversarial Robustness

Adversarial Robustness evaluates the vulnerabilities of machine learning models under various types of adversarial attacks.

Papers

Showing 151200 of 1746 papers

TitleStatusHype
Distilling Robust and Non-Robust Features in Adversarial Examples by Information BottleneckCode1
An Adaptive Orthogonal Convolution Scheme for Efficient and Flexible CNN ArchitecturesCode1
Drop-Bottleneck: Learning Discrete Compressed Representation for Noise-Robust ExplorationCode1
Efficient Exact Verification of Binarized Neural NetworksCode1
Adversarial Robustness Limits via Scaling-Law and Human-Alignment StudiesCode1
Eliminating Catastrophic Overfitting Via Abnormal Adversarial Examples RegularizationCode1
Adversarial Robustness of Bottleneck Injected Deep Neural Networks for Task-Oriented CommunicationCode1
Enhancing Adversarial Robustness for Deep Metric LearningCode1
Adversarial Robustness of Deep Convolutional Candlestick LearnerCode1
On the Adversarial Robustness of Vision TransformersCode1
Are Transformers More Robust Than CNNs?Code1
Evaluating the Adversarial Robustness of Adaptive Test-time DefensesCode1
Certified Training: Small Boxes are All You NeedCode1
Explainability-Aware One Point Attack for Point Cloud Neural NetworksCode1
A Pilot Study of Query-Free Adversarial Attack against Stable DiffusionCode1
A Perturbation-Constrained Adversarial Attack for Evaluating the Robustness of Optical FlowCode1
ARAE: Adversarially Robust Training of Autoencoders Improves Novelty DetectionCode1
Are socially-aware trajectory prediction models really socially-aware?Code1
Certified Adversarial Robustness via Randomized SmoothingCode1
DRSM: De-Randomized Smoothing on Malware Classifier Providing Certified RobustnessCode1
FedNest: Federated Bilevel, Minimax, and Compositional OptimizationCode1
Few-Shot Adversarial Prompt Learning on Vision-Language ModelsCode1
Flooding-X: Improving BERT’s Resistance to Adversarial Attacks via Loss-Restricted Fine-TuningCode1
A Regularization Method to Improve Adversarial Robustness of Neural Networks for ECG Signal ClassificationCode1
Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language ModelsCode1
CFA: Class-wise Calibrated Fair Adversarial TrainingCode1
Adversarial Robustness of Representation Learning for Knowledge GraphsCode1
Guardians of Image Quality: Benchmarking Defenses Against Adversarial Attacks on Image Quality MetricsCode1
Adversarial Robustness via Random Projection FiltersCode1
Helper-based Adversarial Training: Reducing Excessive Margin to Achieve a Better Accuracy vs. Robustness Trade-offCode1
A Self-supervised Approach for Adversarial RobustnessCode1
Hold me tight! Influence of discriminative features on deep network boundariesCode1
Adversarially-Aware Robust Object DetectorCode1
A Unified Game-Theoretic Interpretation of Adversarial RobustnessCode1
Adversarial Robustness as a Prior for Learned RepresentationsCode1
Adversarial Robustness on In- and Out-Distribution Improves ExplainabilityCode1
Attacks Which Do Not Kill Training Make Adversarial Learning StrongerCode1
A Unified Framework for Adversarial Attack and Defense in Constrained Feature SpaceCode1
A Unified Analysis of Mixed Sample Data Augmentation: A Loss Function PerspectiveCode1
CBA: Contextual Background Attack against Optical Aerial Detection in the Physical WorldCode1
Adversarial Attacks on Graph Classifiers via Bayesian OptimisationCode1
Adversarial Robustness Against the Union of Multiple Threat ModelsCode1
Bag of Tricks for Adversarial TrainingCode1
CausalAdv: Adversarial Robustness through the Lens of CausalityCode1
Improving Adversarial Robustness by Enforcing Local and Global CompactnessCode1
Cauchy-Schwarz Divergence Information Bottleneck for RegressionCode1
Adversarial Robustness under Long-Tailed DistributionCode1
Benchmarking Adversarial Robustness on Image ClassificationCode1
(Certified!!) Adversarial Robustness for Free!Code1
CLIP is Strong Enough to Fight Back: Test-time Counterattacks towards Zero-shot Adversarial Robustness of CLIPCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeBERTa (single model)Accuracy0.61Unverified
2ALBERT (single model)Accuracy0.59Unverified
3T5 (single model)Accuracy0.57Unverified
4SMART_RoBERTa (single model)Accuracy0.54Unverified
5FreeLB (single model)Accuracy0.5Unverified
6RoBERTa (single model)Accuracy0.5Unverified
7InfoBERT (single model)Accuracy0.46Unverified
8ELECTRA (single model)Accuracy0.42Unverified
9BERT (single model)Accuracy0.34Unverified
10SMART_BERT (single model)Accuracy0.3Unverified
#ModelMetricClaimedVerifiedStatus
1Mixed classifierAccuracy95.23Unverified
2Stochastic-LWTA/PGD/WideResNet-34-10Accuracy92.26Unverified
3Stochastic-LWTA/PGD/WideResNet-34-5Accuracy91.88Unverified
4GLOT-DRAccuracy84.13Unverified
5TRADES-ANCRA/ResNet18Accuracy81.7Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet-50 (SGD, Cosine)Accuracy77.4Unverified
2ResNet-50 (SGD, Step)Accuracy76.9Unverified
3DeiT-S (AdamW, Cosine)Accuracy76.8Unverified
4ResNet-50 (AdamW, Cosine)Accuracy76.4Unverified
#ModelMetricClaimedVerifiedStatus
1DeiT-S (AdamW, Cosine)Accuracy12.2Unverified
2ResNet-50 (SGD, Cosine)Accuracy3.3Unverified
3ResNet-50 (SGD, Step)Accuracy3.2Unverified
4ResNet-50 (AdamW, Cosine)Accuracy3.1Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet-50 (AdamW, Cosine)mean Corruption Error (mCE)59.3Unverified
2ResNet-50 (SGD, Step)mean Corruption Error (mCE)57.9Unverified
3ResNet-50 (SGD, Cosine)mean Corruption Error (mCE)56.9Unverified
4DeiT-S (AdamW, Cosine)mean Corruption Error (mCE)48Unverified
#ModelMetricClaimedVerifiedStatus
1DeiT-S (AdamW, Cosine)Accuracy13Unverified
2ResNet-50 (SGD, Cosine)Accuracy8.4Unverified
3ResNet-50 (SGD, Step)Accuracy8.3Unverified
4ResNet-50 (AdamW, Cosine)Accuracy8.1Unverified
#ModelMetricClaimedVerifiedStatus
1Mixed ClassifierClean Accuracy85.21Unverified
2ResNet18/MART-ANCRAClean Accuracy60.1Unverified