SOTAVerified

Adversarial Robustness

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

Papers

Showing 651700 of 1746 papers

TitleStatusHype
A Training Rate and Survival Heuristic for Inference and Robustness Evaluation (TRASHFIRE)Code0
Evaluation of Hate Speech Detection Using Large Language Models and Geographical ContextualizationCode0
Hyper-parameter Tuning for Adversarially Robust ModelsCode0
Evolution-based Region Adversarial Prompt Learning for Robustness Enhancement in Vision-Language ModelsCode0
Improved Adversarial Training Through Adaptive Instance-wise Loss SmoothingCode0
Does language help generalization in vision models?Code0
An Empirical Study on the Relation between Network Interpretability and Adversarial RobustnessCode0
Expanding Scope: Adapting English Adversarial Attacks to ChineseCode0
Adaptive Randomized Smoothing: Certified Adversarial Robustness for Multi-Step DefencesCode0
APRICOT: A Dataset of Physical Adversarial Attacks on Object DetectionCode0
A practical approach to evaluating the adversarial distance for machine learning classifiersCode0
Human Eyes Inspired Recurrent Neural Networks are More Robust Against Adversarial NoisesCode0
Approximate Manifold Defense Against Multiple Adversarial PerturbationsCode0
Explaining Adversarial Vulnerability with a Data Sparsity HypothesisCode0
Model-tuning Via Prompts Makes NLP Models Adversarially RobustCode0
Adversarial Concurrent Training: Optimizing Robustness and Accuracy Trade-off of Deep Neural NetworksCode0
Improved Diffusion-based Generative Model with Better Adversarial RobustnessCode0
Disentangling Adversarial Robustness in Directions of the Data ManifoldCode0
Disentangling Adversarial Robustness and GeneralizationCode0
A PAC-Bayes Analysis of Adversarial RobustnessCode0
Exploring Adversarial Examples and Adversarial Robustness of Convolutional Neural Networks by Mutual InformationCode0
Exploring Adversarially Robust Training for Unsupervised Domain AdaptationCode0
Hierarchical Distribution-Aware Testing of Deep LearningCode0
The interplay of robustness and generalization in quantum machine learningCode0
Diffusion Denoised Smoothing for Certified and Adversarial Robust Out-Of-Distribution DetectionCode0
Hardening DNNs against Transfer Attacks during Network Compression using Greedy Adversarial PruningCode0
Diffusion-based Adversarial Purification for Intrusion DetectionCode0
How many perturbations break this model? Evaluating robustness beyond adversarial accuracyCode0
Global-Local Regularization Via Distributional RobustnessCode0
Gradient-Free Adversarial Attacks for Bayesian Neural NetworksCode0
DiffPAD: Denoising Diffusion-based Adversarial Patch DecontaminationCode0
Different Spectral Representations in Optimized Artificial Neural Networks and BrainsCode0
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 Samples in Mini-Batches May Be Detrimental To Adversarial RobustnessCode0
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
Deterministic Gaussian Averaged Neural NetworksCode0
Detection Defenses: An Empty Promise against Adversarial Patch Attacks on Optical FlowCode0
Annealing Self-Distillation Rectification Improves Adversarial TrainingCode0
Dense Hopfield Networks in the Teacher-Student SettingCode0
AutoZOOM: Autoencoder-based Zeroth Order Optimization Method for Attacking Black-box Neural NetworksCode0
Expressive Losses for Verified Robustness via Convex CombinationsCode0
GAT: Guided Adversarial Training with Pareto-optimal Auxiliary TasksCode0
Adversarial Fine-tuning of Compressed Neural Networks for Joint Improvement of Robustness and EfficiencyCode0
On the human-recognizability phenomenon of adversarially trained deep image classifiersCode0
On the Importance of Backbone to the Adversarial Robustness of Object DetectorsCode0
GenAttack: Practical Black-box Attacks with Gradient-Free OptimizationCode0
A New Dataset Based on Images Taken by Blind People for Testing the Robustness of Image Classification Models Trained for ImageNet CategoriesCode0
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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