SOTAVerified

Adversarial Robustness

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

Papers

Showing 351400 of 1746 papers

TitleStatusHype
Gradient-Free Adversarial Attacks for Bayesian Neural NetworksCode0
Hierarchical Distribution-Aware Testing of Deep LearningCode0
Improved Diffusion-based Generative Model with Better Adversarial RobustnessCode0
Adversaries With Incentives: A Strategic Alternative to Adversarial RobustnessCode0
Adversarial Machine Learning in Latent Representations of Neural NetworksCode0
Model Compression with Adversarial Robustness: A Unified Optimization FrameworkCode0
Generating Adversarial Examples with Adversarial NetworksCode0
Generating Adversarial Samples in Mini-Batches May Be Detrimental To Adversarial RobustnessCode0
Generative Max-Mahalanobis Classifiers for Image Classification, Generation and MoreCode0
GAT: Guided Adversarial Training with Pareto-optimal Auxiliary TasksCode0
Adversarial Training and Robustness for Multiple PerturbationsCode0
GenAttack: Practical Black-box Attacks with Gradient-Free OptimizationCode0
Adversarial Robustness with Semi-Infinite Constrained LearningCode0
Gated Information Bottleneck for Generalization in Sequential EnvironmentsCode0
Adversarial Robustness with Non-uniform PerturbationsCode0
Adversarially Robust Spiking Neural Networks Through ConversionCode0
Adversarial Robustness vs. Model Compression, or Both?Code0
Get Fooled for the Right Reason: Improving Adversarial Robustness through a Teacher-guided Curriculum Learning ApproachCode0
Finding Biological Plausibility for Adversarially Robust Features via Metameric TasksCode0
Adversarial robustness via robust low rank representationsCode0
Adversarially Robust One-class Novelty DetectionCode0
FI-ODE: Certifiably Robust Forward Invariance in Neural ODEsCode0
Adversarial Robustness via Fisher-Rao RegularizationCode0
Feature Statistics with Uncertainty Help Adversarial RobustnessCode0
Advancing Adversarial Robustness in GNeRFs: The IL2-NeRF AttackCode0
Bridging the Performance Gap between FGSM and PGD Adversarial TrainingCode0
Adversarial Robustness Verification and Attack Synthesis in Stochastic SystemsCode0
Feature Denoising for Improving Adversarial RobustnessCode0
Bridging Robustness and Generalization Against Word Substitution Attacks in NLP via the Growth Bound Matrix ApproachCode0
Bridging Adversarial Robustness and Gradient InterpretabilityCode0
Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin AttackCode0
Fast Adversarial Robustness Certification of Nearest Prototype Classifiers for Arbitrary SeminormsCode0
Adversarial Robustness through the Lens of Convolutional FiltersCode0
Fake It Until You Break It: On the Adversarial Robustness of AI-generated Image DetectorsCode0
Fast Adversarial Training with Smooth ConvergenceCode0
Boosting Adversarial Training via Fisher-Rao Norm-based RegularizationCode0
Expressive Losses for Verified Robustness via Convex CombinationsCode0
Boosting Adversarial Robustness using Feature Level Stochastic SmoothingCode0
Adversarially Robust Learning with Optimal Transport Regularized DivergencesCode0
Scaling Trends in Language Model RobustnessCode0
Expressivity of Graph Neural Networks Through the Lens of Adversarial RobustnessCode0
FaiR-N: Fair and Robust Neural Networks for Structured DataCode0
Boosting Accuracy and Robustness of Student Models via Adaptive Adversarial DistillationCode0
Bounding the Expected Robustness of Graph Neural Networks Subject to Node Feature AttacksCode0
BNN-DP: Robustness Certification of Bayesian Neural Networks via Dynamic ProgrammingCode0
FairDeFace: Evaluating the Fairness and Adversarial Robustness of Face Obfuscation MethodsCode0
Efficiently Training Low-Curvature Neural NetworksCode0
Bridging the Gap Between Adversarial Robustness and Optimization BiasCode0
Give me a hint: Can LLMs take a hint to solve math problems?Code0
Improved Robustness Against Adaptive Attacks With Ensembles and Error-Correcting Output CodesCode0
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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