SOTAVerified

Adversarial Robustness

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

Papers

Showing 12511300 of 1746 papers

TitleStatusHype
Robust Adversarial Classification via Abstaining0
Adversarial Attacks and Defenses for Speech Recognition Systems0
Learning Lipschitz Feedback Policies from Expert Demonstrations: Closed-Loop Guarantees, Generalization and Robustness0
Towards Understanding Adversarial Robustness of Optical Flow NetworksCode0
Class-Aware Robust Adversarial Training for Object Detection0
On the Adversarial Robustness of Vision TransformersCode1
Ensemble-in-One: Learning Ensemble within Random Gated Networks for Enhanced Adversarial Robustness0
Drop-Bottleneck: Learning Discrete Compressed Representation for Noise-Robust ExplorationCode1
Interpretable Deep Learning: Interpretation, Interpretability, Trustworthiness, and BeyondCode1
Generating Adversarial Computer Programs using Optimized ObfuscationsCode1
Constant Random Perturbations Provide Adversarial Robustness with Minimal Effect on AccuracyCode0
A Unified Game-Theoretic Interpretation of Adversarial RobustnessCode1
Improving Adversarial Robustness via Channel-wise Activation SuppressingCode1
Reframing Neural Networks: Deep Structure in Overcomplete Representations0
Constrained Learning with Non-Convex Losses0
Improving Transformation-based Defenses against Adversarial Examples with First-order Perturbations0
Consistency Regularization for Adversarial RobustnessCode1
Improving Global Adversarial Robustness Generalization With Adversarially Trained GAN0
CLAIMED, a visual and scalable component library for Trusted AICode2
Structure-Preserving Progressive Low-rank Image Completion for Defending Adversarial Attacks0
Shift Invariance Can Reduce Adversarial RobustnessCode0
Smoothness Analysis of Adversarial Training0
Evaluating the Robustness of Geometry-Aware Instance-Reweighted Adversarial TrainingCode0
Fixing Data Augmentation to Improve Adversarial RobustnessCode1
A Multiclass Boosting Framework for Achieving Fast and Provable Adversarial Robustness0
Mind the box: l_1-APGD for sparse adversarial attacks on image classifiers0
Explaining Adversarial Vulnerability with a Data Sparsity HypothesisCode0
Adversarial Information Bottleneck0
Fast Minimum-norm Adversarial Attacks through Adaptive Norm ConstraintsCode2
Towards Robust Graph Contrastive Learning0
Multiplicative Reweighting for Robust Neural Network OptimizationCode0
Adversarial Robustness with Non-uniform PerturbationsCode0
Non-Singular Adversarial Robustness of Neural Networks0
The Effects of Image Distribution and Task on Adversarial Robustness0
On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-LearningCode1
A PAC-Bayes Analysis of Adversarial RobustnessCode0
Center Smoothing: Certified Robustness for Networks with Structured OutputsCode0
Effective and Efficient Vote Attack on Capsule NetworksCode0
Random Projections for Improved Adversarial Robustness0
Make Sure You're Unsure: A Framework for Verifying Probabilistic SpecificationsCode1
Bridging the Gap Between Adversarial Robustness and Optimization BiasCode0
Improving Hierarchical Adversarial Robustness of Deep Neural Networks0
And/or trade-off in artificial neurons: impact on adversarial robustness0
CAP-GAN: Towards Adversarial Robustness with Cycle-consistent Attentional Purification0
Guided Interpolation for Adversarial Training0
Data Quality Matters For Adversarial Training: An Empirical StudyCode0
Generating Structured Adversarial Attacks Using Frank-Wolfe Method0
Exploring Adversarial Robustness of Deep Metric LearningCode0
Adversarial Robustness: What fools you makes you stronger0
CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature SelectionCode1
Show:102550
← PrevPage 26 of 35Next →

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