SOTAVerified

Adversarial Robustness

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

Papers

Showing 501550 of 1746 papers

TitleStatusHype
Data Quality Matters For Adversarial Training: An Empirical StudyCode0
DAT: Improving Adversarial Robustness via Generative Amplitude Mix-up in Frequency DomainCode0
IB-RAR: Information Bottleneck as Regularizer for Adversarial RobustnessCode0
BEARD: Benchmarking the Adversarial Robustness for Dataset DistillationCode0
Bayesian Inference with Certifiable Adversarial RobustnessCode0
Deceptive Fairness Attacks on Graphs via Meta LearningCode0
Batch Normalization Increases Adversarial Vulnerability and Decreases Adversarial Transferability: A Non-Robust Feature PerspectiveCode0
Adversarial Robustness of Prompt-based Few-Shot Learning for Natural Language UnderstandingCode0
Hyper-parameter Tuning for Adversarially Robust ModelsCode0
Improving Adversarial Robust Fairness via Anti-Bias Soft Label DistillationCode0
Improving Robustness of Convolutional Neural Networks Using Element-Wise Activation ScalingCode0
Adversarial Fine-tuning of Compressed Neural Networks for Joint Improvement of Robustness and EfficiencyCode0
AutoZOOM: Autoencoder-based Zeroth Order Optimization Method for Attacking Black-box Neural NetworksCode0
Automated Repair of Neural NetworksCode0
Adversarial Robustness of Neural-Statistical Features in Detection of Generative TransformersCode0
Adversarial Robustness of MR Image Reconstruction under Realistic PerturbationsCode0
Adaptive Smoothness-weighted Adversarial Training for Multiple Perturbations with Its Stability AnalysisCode0
Hierarchical Distribution-Aware Testing of Deep LearningCode0
Adversarial Feature DesensitizationCode0
GREAT Score: Global Robustness Evaluation of Adversarial Perturbation using Generative ModelsCode0
Hardening DNNs against Transfer Attacks during Network Compression using Greedy Adversarial PruningCode0
Improving Adversarial Robustness by Putting More Regularizations on Less Robust SamplesCode0
Human Eyes Inspired Recurrent Neural Networks are More Robust Against Adversarial NoisesCode0
Generative Max-Mahalanobis Classifiers for Image Classification, Generation and MoreCode0
Get Fooled for the Right Reason: Improving Adversarial Robustness through a Teacher-guided Curriculum Learning ApproachCode0
Generating Adversarial Examples with Adversarial NetworksCode0
On Adversarial Robustness of Point Cloud Semantic SegmentationCode0
Generating Adversarial Samples in Mini-Batches May Be Detrimental To Adversarial RobustnessCode0
Give me a hint: Can LLMs take a hint to solve math problems?Code0
GenAttack: Practical Black-box Attacks with Gradient-Free OptimizationCode0
Adaptive Randomized Smoothing: Certified Adversarial Robustness for Multi-Step DefencesCode0
Gated Information Bottleneck for Generalization in Sequential EnvironmentsCode0
Adversarial Robustness of Deep Learning-Based Malware Detectors via (De)Randomized SmoothingCode0
GAT: Guided Adversarial Training with Pareto-optimal Auxiliary TasksCode0
Global-Local Regularization Via Distributional RobustnessCode0
Adversarial Examples for k-Nearest Neighbor Classifiers Based on Higher-Order Voronoi DiagramsCode0
A Training Rate and Survival Heuristic for Inference and Robustness Evaluation (TRASHFIRE)Code0
Adversarial Robustness of Deep Learning Models for Inland Water Body Segmentation from SAR ImagesCode0
Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model GeneralizationCode0
FI-ODE: Certifiably Robust Forward Invariance in Neural ODEsCode0
Efficiently Training Low-Curvature Neural NetworksCode0
Gradient-Free Adversarial Attacks for Bayesian Neural NetworksCode0
A Study on Adversarial Robustness of Discriminative Prototypical LearningCode0
Tight Certificates of Adversarial Robustness for Randomly Smoothed ClassifiersCode0
Adversarial robustness of VAEs through the lens of local geometryCode0
Weight-Covariance Alignment for Adversarially Robust Neural NetworksCode0
Adversarial Examples for Evaluating Math Word Problem SolversCode0
Feature Statistics with Uncertainty Help Adversarial RobustnessCode0
Feature Denoising for Improving Adversarial RobustnessCode0
Assaying Out-Of-Distribution Generalization in Transfer LearningCode0
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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