SOTAVerified

Adversarial Robustness

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

Papers

Showing 551600 of 1746 papers

TitleStatusHype
Assaying Out-Of-Distribution Generalization in Transfer LearningCode0
IBP Regularization for Verified Adversarial Robustness via Branch-and-BoundCode0
Impact of Architectural Modifications on Deep Learning Adversarial RobustnessCode0
Improved Adversarial Training Through Adaptive Instance-wise Loss SmoothingCode0
Human Eyes Inspired Recurrent Neural Networks are More Robust Against Adversarial NoisesCode0
A Simple Approach to Adversarial Robustness in Few-shot Image ClassificationCode0
Hyper-parameter Tuning for Adversarially Robust ModelsCode0
Exploring the Landscape of Spatial RobustnessCode0
AROID: Improving Adversarial Robustness Through Online Instance-Wise Data AugmentationCode0
A Brain-Inspired Regularizer for Adversarial RobustnessCode0
Improved Diffusion-based Generative Model with Better Adversarial RobustnessCode0
A Robust Backpropagation-Free Framework for ImagesCode0
How to compare adversarial robustness of classifiers from a global perspectiveCode0
Adversarial Robustness in Multi-Task Learning: Promises and IllusionsCode0
Efficient Robustness Assessment via Adversarial Spatial-Temporal Focus on VideosCode0
Hierarchical Distribution-Aware Testing of Deep LearningCode0
Adversarial Ensemble Training by Jointly Learning Label Dependencies and Member ModelsCode0
Efficient Contrastive Explanations on DemandCode0
Are Large Language Models Really Bias-Free? Jailbreak Prompts for Assessing Adversarial Robustness to Bias ElicitationCode0
A Curious Case of Searching for the Correlation between Training Data and Adversarial Robustness of Transformer Textual ModelsCode0
Hardening DNNs against Transfer Attacks during Network Compression using Greedy Adversarial PruningCode0
Global-Local Regularization Via Distributional RobustnessCode0
Get Fooled for the Right Reason: Improving Adversarial Robustness through a Teacher-guided Curriculum Learning ApproachCode0
Gradient-Free Adversarial Attacks for Bayesian Neural NetworksCode0
Generating Adversarial Samples in Mini-Batches May Be Detrimental To Adversarial RobustnessCode0
Dynamic Time Warping based Adversarial Framework for Time-Series DomainCode0
Adversarial robustness of amortized Bayesian inferenceCode0
Generative Max-Mahalanobis Classifiers for Image Classification, Generation and MoreCode0
GenAttack: Practical Black-box Attacks with Gradient-Free OptimizationCode0
Architectural Resilience to Foreground-and-Background Adversarial NoiseCode0
Gated Information Bottleneck for Generalization in Sequential EnvironmentsCode0
GAT: Guided Adversarial Training with Pareto-optimal Auxiliary TasksCode0
Adversarial Robustness Guarantees for Gaussian ProcessesCode0
EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial ExamplesCode0
Are Generative Classifiers More Robust to Adversarial Attacks?Code0
Effective and Efficient Vote Attack on Capsule NetworksCode0
Generating Adversarial Examples with Adversarial NetworksCode0
Adversarial Robustness Guarantees for Random Deep Neural NetworksCode0
Are Labels Required for Improving Adversarial Robustness?Code0
Give me a hint: Can LLMs take a hint to solve math problems?Code0
GREAT Score: Global Robustness Evaluation of Adversarial Perturbation using Generative ModelsCode0
Improved Robustness Against Adaptive Attacks With Ensembles and Error-Correcting Output CodesCode0
Characterizing Data Point Vulnerability via Average-Case RobustnessCode0
Adversarial Robustness for Visual Grounding of Multimodal Large Language ModelsCode0
Do Perceptually Aligned Gradients Imply Adversarial Robustness?Code0
Finding Biological Plausibility for Adversarially Robust Features via Metameric TasksCode0
Don't Look into the Sun: Adversarial Solarization Attacks on Image ClassifiersCode0
FI-ODE: Certifiably Robust Forward Invariance in Neural ODEsCode0
Feature Statistics with Uncertainty Help Adversarial RobustnessCode0
Level Up with ML Vulnerability Identification: Leveraging Domain Constraints in Feature Space for Robust Android Malware DetectionCode0
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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