SOTAVerified

Adversarial Robustness

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

Papers

Showing 401450 of 1746 papers

TitleStatusHype
Bridging Adversarial Robustness and Gradient InterpretabilityCode0
Advancing Adversarial Robustness in GNeRFs: The IL2-NeRF AttackCode0
Building Robust Ensembles via Margin BoostingCode0
CAAD 2018: Generating Transferable Adversarial ExamplesCode0
Generating Adversarial Samples in Mini-Batches May Be Detrimental To Adversarial RobustnessCode0
Adversarial Robustness via Fisher-Rao RegularizationCode0
Bounding the Expected Robustness of Graph Neural Networks Subject to Node Feature AttacksCode0
CAMP in the Odyssey: Provably Robust Reinforcement Learning with Certified Radius MaximizationCode0
GAT: Guided Adversarial Training with Pareto-optimal Auxiliary TasksCode0
Hardening DNNs against Transfer Attacks during Network Compression using Greedy Adversarial PruningCode0
Adversarial Robustness through the Lens of Convolutional FiltersCode0
GenAttack: Practical Black-box Attacks with Gradient-Free OptimizationCode0
Generative Max-Mahalanobis Classifiers for Image Classification, Generation and MoreCode0
Boosting Adversarial Training via Fisher-Rao Norm-based RegularizationCode0
Boosting Adversarial Robustness using Feature Level Stochastic SmoothingCode0
Adversarially Robust Learning with Optimal Transport Regularized DivergencesCode0
Efficiently Training Low-Curvature Neural NetworksCode0
Boosting Accuracy and Robustness of Student Models via Adaptive Adversarial DistillationCode0
BNN-DP: Robustness Certification of Bayesian Neural Networks via Dynamic ProgrammingCode0
Carefully Blending Adversarial Training, Purification, and Aggregation Improves Adversarial RobustnessCode0
Gated Information Bottleneck for Generalization in Sequential EnvironmentsCode0
Get Fooled for the Right Reason: Improving Adversarial Robustness through a Teacher-guided Curriculum Learning ApproachCode0
Improving Adversarial Robustness via Guided Complement EntropyCode0
Biologically Inspired Mechanisms for Adversarial RobustnessCode0
CausAdv: A Causal-based Framework for Detecting Adversarial ExamplesCode0
IBP Regularization for Verified Adversarial Robustness via Branch-and-BoundCode0
Adversarial Training and Robustness for Multiple PerturbationsCode0
Feature Statistics with Uncertainty Help Adversarial RobustnessCode0
Implicit Generative Modeling of Random Noise during Training for Adversarial RobustnessCode0
Improved Adversarial Training Through Adaptive Instance-wise Loss SmoothingCode0
Adversarial Robustness Study of Convolutional Neural Network for Lumbar Disk Shape Reconstruction from MR imagesCode0
Feature Denoising for Improving Adversarial RobustnessCode0
Fast Adversarial Training with Smooth ConvergenceCode0
Center Smoothing: Certified Robustness for Networks with Structured OutputsCode0
Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin AttackCode0
Model Compression with Adversarial Robustness: A Unified Optimization FrameworkCode0
A Deep Dive into Adversarial Robustness in Zero-Shot LearningCode0
Beyond Pretrained Features: Noisy Image Modeling Provides Adversarial DefenseCode0
Fast Adversarial Robustness Certification of Nearest Prototype Classifiers for Arbitrary SeminormsCode0
Beyond One-Hot-Encoding: Injecting Semantics to Drive Image ClassifiersCode0
Beyond Model Interpretability: On the Faithfulness and Adversarial Robustness of Contrastive Textual ExplanationsCode0
Expressivity of Graph Neural Networks Through the Lens of Adversarial RobustnessCode0
Expressive Losses for Verified Robustness via Convex CombinationsCode0
FairDeFace: Evaluating the Fairness and Adversarial Robustness of Face Obfuscation MethodsCode0
FaiR-N: Fair and Robust Neural Networks for Structured DataCode0
Adversarial Robustness of VAEs across Intersectional SubgroupsCode0
Adversarial Attack Generation Empowered by Min-Max OptimizationCode0
Scaling Trends in Language Model RobustnessCode0
Certifying Joint Adversarial Robustness for Model EnsemblesCode0
Fake It Until You Break It: On the Adversarial Robustness of AI-generated Image DetectorsCode0
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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