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
Improving Adversarial Robustness via Mutual Information EstimationCode1
Decoupled Adversarial Contrastive Learning for Self-supervised Adversarial RobustnessCode1
Tailoring Self-Supervision for Supervised LearningCode1
Adversarial Contrastive Learning via Asymmetric InfoNCECode1
CARBEN: Composite Adversarial Robustness BenchmarkCode1
Distance Learner: Incorporating Manifold Prior to Model TrainingCode1
Adversarially-Aware Robust Object DetectorCode1
Removing Batch Normalization Boosts Adversarial TrainingCode1
Robust Deep Reinforcement Learning through Bootstrapped Opportunistic CurriculumCode1
(Certified!!) Adversarial Robustness for Free!Code1
Towards Adversarial Attack on Vision-Language Pre-training ModelsCode1
Understanding Robust Overfitting of Adversarial Training and BeyondCode1
Adversarial Vulnerability of Randomized EnsemblesCode1
CARLA-GeAR: a Dataset Generator for a Systematic Evaluation of Adversarial Robustness of Vision ModelsCode1
FedNest: Federated Bilevel, Minimax, and Compositional OptimizationCode1
Flooding-X: Improving BERT’s Resistance to Adversarial Attacks via Loss-Restricted Fine-TuningCode1
Engineering flexible machine learning systems by traversing functionally-invariant pathsCode1
Masking Adversarial Damage: Finding Adversarial Saliency for Robust and Sparse NetworkCode1
Distilling Robust and Non-Robust Features in Adversarial Examples by Information BottleneckCode1
How to Robustify Black-Box ML Models? A Zeroth-Order Optimization PerspectiveCode1
A Perturbation-Constrained Adversarial Attack for Evaluating the Robustness of Optical FlowCode1
Practical Evaluation of Adversarial Robustness via Adaptive Auto AttackCode1
Joint rotational invariance and adversarial training of a dual-stream Transformer yields state of the art Brain-Score for Area V4Code1
ImageNet-Patch: A Dataset for Benchmarking Machine Learning Robustness against Adversarial PatchesCode1
Enhancing Adversarial Robustness for Deep Metric LearningCode1
Evaluating the Adversarial Robustness of Adaptive Test-time DefensesCode1
White-Box Attacks on Hate-speech BERT Classifiers in German with Explicit and Implicit Character Level DefenseCode1
Towards Compositional Adversarial Robustness: Generalizing Adversarial Training to Composite Semantic PerturbationsCode1
LyaNet: A Lyapunov Framework for Training Neural ODEsCode1
The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse TrainingCode1
Rate Coding or Direct Coding: Which One is Better for Accurate, Robust, and Energy-efficient Spiking Neural Networks?Code1
GARNET: Reduced-Rank Topology Learning for Robust and Scalable Graph Neural NetworksCode1
Adversarial vulnerability of powerful near out-of-distribution detectionCode1
On Adversarial Robustness of Trajectory Prediction for Autonomous VehiclesCode1
On the Real-World Adversarial Robustness of Real-Time Semantic Segmentation Models for Autonomous DrivingCode1
How Should Pre-Trained Language Models Be Fine-Tuned Towards Adversarial Robustness?Code1
PixMix: Dreamlike Pictures Comprehensively Improve Safety MeasuresCode1
Segment and Complete: Defending Object Detectors against Adversarial Patch Attacks with Robust Patch DetectionCode1
Decision-based Black-box Attack Against Vision Transformers via Patch-wise Adversarial RemovalCode1
Stochastic Local Winner-Takes-All Networks Enable Profound Adversarial RobustnessCode1
A Unified Framework for Adversarial Attack and Defense in Constrained Feature SpaceCode1
Training Efficiency and Robustness in Deep LearningCode1
Is RobustBench/AutoAttack a suitable Benchmark for Adversarial Robustness?Code1
Towards a Unified Game-Theoretic View of Adversarial Perturbations and RobustnessCode1
Adversarial Attacks on Graph Classifiers via Bayesian OptimisationCode1
ExCon: Explanation-driven Supervised Contrastive Learning for Image ClassificationCode1
Are Transformers More Robust Than CNNs?Code1
Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine LearningCode1
A Unified Game-Theoretic Interpretation of Adversarial RobustnessCode1
Adversarial Attacks on Graph Classification via Bayesian OptimisationCode1
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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