SOTAVerified

Adversarial Robustness

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

Papers

Showing 16011650 of 1746 papers

TitleStatusHype
Towards A Unified Min-Max Framework for Adversarial Exploration and Robustness0
Scalable Neural Learning for Verifiable Consistency with Temporal Specifications0
Invariance vs Robustness of Neural Networks0
Mixup Inference: Better Exploiting Mixup to Defend Adversarial AttacksCode0
Sign-OPT: A Query-Efficient Hard-label Adversarial AttackCode0
Absum: Simple Regularization Method for Reducing Structural Sensitivity of Convolutional Neural Networks0
Training Robust Deep Neural Networks via Adversarial Noise Propagation0
Interpreting and Improving Adversarial Robustness of Deep Neural Networks with Neuron Sensitivity0
An Empirical Study towards Characterizing Deep Learning Development and Deployment across Different Frameworks and Platforms0
Towards Model-Agnostic Adversarial Defenses using Adversarially Trained Autoencoders0
Feedback Learning for Improving the Robustness of Neural Networks0
Neural Belief Reasoner0
Adversarial Robustness Against the Union of Multiple Perturbation ModelsCode1
Are Adversarial Robustness and Common Perturbation Robustness Independent Attributes ?0
Metric Learning for Adversarial RobustnessCode0
Adversarial Robustness of Similarity-Based Link Prediction0
Improving Adversarial Robustness via Attention and Adversarial Logit Pairing0
Testing Robustness Against Unforeseen AdversariesCode0
Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic DefensesCode0
On the Adversarial Robustness of Subspace Learning0
Adversarial Neural Pruning with Latent Vulnerability SuppressionCode0
On the Adversarial Robustness of Neural Networks without Weight Transport0
Improved Adversarial Robustness by Reducing Open Space Risk via Tent Activations0
Adversarial Robustness Curves0
Adversarial Test on Learnable Image Encryption0
Understanding Adversarial Robustness: The Trade-off between Minimum and Average Margin0
Understanding Adversarial Robustness Through Loss Landscape Geometries0
Robustness properties of Facebook's ResNeXt WSL modelsCode0
Adversarial Robustness through Local Linearization0
Adversarial Robustness via Label-Smoothing0
The Attack Generator: A Systematic Approach Towards Constructing Adversarial Attacks0
Interpolated Adversarial Training: Achieving Robust Neural Networks without Sacrificing Too Much AccuracyCode0
Adversarial Robustness Assessment: Why both L_0 and L_ Attacks Are NecessaryCode0
Towards Compact and Robust Deep Neural Networks0
Tight Certificates of Adversarial Robustness for Randomly Smoothed ClassifiersCode0
Topology Attack and Defense for Graph Neural Networks: An Optimization PerspectiveCode0
Intriguing properties of adversarial training at scale0
Improved Adversarial Robustness via Logit Regularization Methods0
Adversarial Attack Generation Empowered by Min-Max OptimizationCode0
Provably Robust Boosted Decision Stumps and Trees against Adversarial AttacksCode0
Image Synthesis with a Single (Robust) ClassifierCode0
Understanding Adversarial Behavior of DNNs by Disentangling Non-Robust and Robust Components in Performance Metric0
MNIST-C: A Robustness Benchmark for Computer VisionCode1
Adversarial Robustness as a Prior for Learned RepresentationsCode1
ShieldNets: Defending Against Adversarial Attacks Using Probabilistic Adversarial Robustness0
Unlabeled Data Improves Adversarial RobustnessCode0
Are Labels Required for Improving Adversarial Robustness?Code0
Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial RobustnessCode0
Better Generalization with Adaptive Adversarial Training0
ME-Net: Towards Effective Adversarial Robustness with Matrix EstimationCode0
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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