SOTAVerified

Adversarial Robustness

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

Papers

Showing 251300 of 1746 papers

TitleStatusHype
Adversarial Robustness against Multiple and Single l_p-Threat Models via Quick Fine-Tuning of Robust ClassifiersCode1
Adversarial Attacks on Graph Classification via Bayesian OptimisationCode1
Adversarial Robustness Against the Union of Multiple Perturbation ModelsCode1
A Reputation Mechanism Is All You Need: Collaborative Fairness and Adversarial Robustness in Federated LearningCode1
Adversarial Attacks on Graph Classifiers via Bayesian OptimisationCode1
Cauchy-Schwarz Divergence Information Bottleneck for RegressionCode1
Adversarial Robustness Against the Union of Multiple Threat ModelsCode1
CARLA-GeAR: a Dataset Generator for a Systematic Evaluation of Adversarial Robustness of Vision ModelsCode1
Adversarial Robustness as a Prior for Learned RepresentationsCode1
A Self-supervised Approach for Adversarial RobustnessCode1
Certified Adversarial Robustness via Randomized SmoothingCode1
Certified Training: Small Boxes are All You NeedCode1
CLIP is Strong Enough to Fight Back: Test-time Counterattacks towards Zero-shot Adversarial Robustness of CLIPCode1
Comparing the Robustness of Modern No-Reference Image- and Video-Quality Metrics to Adversarial AttacksCode1
Consistency Regularization for Adversarial RobustnessCode1
Consistency Regularization for Certified Robustness of Smoothed ClassifiersCode1
Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNsCode1
Decision-based Black-box Attack Against Vision Transformers via Patch-wise Adversarial RemovalCode1
Adversarial Attacks on ML Defense Models CompetitionCode1
Decoupled Kullback-Leibler Divergence LossCode1
CBA: Contextual Background Attack against Optical Aerial Detection in the Physical WorldCode1
An Orthogonal Classifier for Improving the Adversarial Robustness of Neural NetworksCode1
Adversarial Robustness for CodeCode1
DF-RAP: A Robust Adversarial Perturbation for Defending against Deepfakes in Real-world Social Network ScenariosCode1
Drawing Robust Scratch Tickets: Subnetworks with Inborn Robustness Are Found within Randomly Initialized NetworksCode1
An Embarrassingly Simple Backdoor Attack on Self-supervised LearningCode1
Efficient Exact Verification of Binarized Neural NetworksCode1
A Perturbation-Constrained Adversarial Attack for Evaluating the Robustness of Optical FlowCode1
A Pilot Study of Query-Free Adversarial Attack against Stable DiffusionCode1
Engineering flexible machine learning systems by traversing functionally-invariant pathsCode1
Exploring Architectural Ingredients of Adversarially Robust Deep Neural NetworksCode1
Enhancing adversarial robustness in Natural Language Inference using explanationsCode1
Enhancing Intrinsic Adversarial Robustness via Feature Pyramid DecoderCode1
Ensemble everything everywhere: Multi-scale aggregation for adversarial robustnessCode1
ARAE: Adversarially Robust Training of Autoencoders Improves Novelty DetectionCode1
ExCon: Explanation-driven Supervised Contrastive Learning for Image ClassificationCode1
Adversarial Robustness: From Self-Supervised Pre-Training to Fine-TuningCode1
A Unified Analysis of Mixed Sample Data Augmentation: A Loss Function PerspectiveCode1
Improve robustness of DNN for ECG signal classification:a noise-to-signal ratio perspectiveCode1
Fast and Scalable Adversarial Training of Kernel SVM via Doubly Stochastic GradientsCode1
Federated Robustness Propagation: Sharing Robustness in Heterogeneous Federated LearningCode1
FedNest: Federated Bilevel, Minimax, and Compositional OptimizationCode1
A Regularization Method to Improve Adversarial Robustness of Neural Networks for ECG Signal ClassificationCode1
Fixing Data Augmentation to Improve Adversarial RobustnessCode1
Attacks Which Do Not Kill Training Make Adversarial Learning StrongerCode1
A Unified Game-Theoretic Interpretation of Adversarial RobustnessCode1
Adversarial Robustness in Graph Neural Networks: A Hamiltonian ApproachCode1
Are socially-aware trajectory prediction models really socially-aware?Code1
On the Duality Between Sharpness-Aware Minimization and Adversarial TrainingCode1
Towards Physically Realizable Adversarial Attacks in Embodied Vision NavigationCode1
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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