SOTAVerified

Adversarial Robustness

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

Papers

Showing 251300 of 1746 papers

TitleStatusHype
Make Sure You're Unsure: A Framework for Verifying Probabilistic SpecificationsCode1
CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature SelectionCode1
Towards Adversarial Robustness of Bayesian Neural Network through Hierarchical Variational InferenceCode1
Better Robustness by More Coverage: Adversarial Training with Mixup Augmentation for Robust Fine-tuningCode1
Composite Adversarial AttacksCode1
Using Feature Alignment Can Improve Clean Average Precision and Adversarial Robustness in Object DetectionCode1
On the Trade-off between Adversarial and Backdoor RobustnessCode1
Regularization with Latent Space Virtual Adversarial TrainingCode1
A Reputation Mechanism Is All You Need: Collaborative Fairness and Adversarial Robustness in Federated LearningCode1
SHIELD: Defending Textual Neural Networks against Multiple Black-Box Adversarial Attacks with Stochastic Multi-Expert PatcherCode1
Adversarial Image Color Transformations in Explicit Color Filter SpaceCode1
Robust Pre-Training by Adversarial Contrastive LearningCode1
RobustBench: a standardized adversarial robustness benchmarkCode1
Shape-Texture Debiased Neural Network TrainingCode1
Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial ExamplesCode1
Bag of Tricks for Adversarial TrainingCode1
Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal MixupCode1
Adversarial Attack and Defense Strategies for Deep Speaker Recognition SystemsCode1
Neural Networks with Recurrent Generative FeedbackCode1
Certifiably Adversarially Robust Detection of Out-of-Distribution DataCode1
Multitask Learning Strengthens Adversarial RobustnessCode1
Understanding Object Detection Through An Adversarial LensCode1
Improving Adversarial Robustness by Enforcing Local and Global CompactnessCode1
RobFR: Benchmarking Adversarial Robustness on Face RecognitionCode1
Proper Network Interpretability Helps Adversarial Robustness in ClassificationCode1
Smooth Adversarial TrainingCode1
Imbalanced Gradients: A Subtle Cause of Overestimated Adversarial RobustnessCode1
Perceptual Adversarial Robustness: Defense Against Unseen Threat ModelsCode1
Achieving robustness in classification using optimal transport with hinge regularizationCode1
A Self-supervised Approach for Adversarial RobustnessCode1
Consistency Regularization for Certified Robustness of Smoothed ClassifiersCode1
Benchmarking Adversarial Robustness on Image ClassificationCode1
Adversarial Robustness of Deep Convolutional Candlestick LearnerCode1
Model-Based Robust Deep Learning: Generalizing to Natural, Out-of-Distribution DataCode1
On Intrinsic Dataset Properties for Adversarial Machine LearningCode1
Improve robustness of DNN for ECG signal classification:a noise-to-signal ratio perspectiveCode1
Evaluating and Improving Adversarial Robustness of Machine Learning-Based Network Intrusion DetectorsCode1
Efficient Exact Verification of Binarized Neural NetworksCode1
Enhancing Intrinsic Adversarial Robustness via Feature Pyramid DecoderCode1
Improving Adversarial Robustness Requires Revisiting Misclassified ExamplesCode1
Bridging Mode Connectivity in Loss Landscapes and Adversarial RobustnessCode1
Adversarial Attack on Deep Learning-Based Splice LocalizationCode1
Adversarial Weight Perturbation Helps Robust GeneralizationCode1
Towards Achieving Adversarial Robustness by Enforcing Feature Consistency Across Bit PlanesCode1
Adversarial Robustness: From Self-Supervised Pre-Training to Fine-TuningCode1
Inherent Adversarial Robustness of Deep Spiking Neural Networks: Effects of Discrete Input Encoding and Non-Linear ActivationsCode1
Adversarial Robustness on In- and Out-Distribution Improves ExplainabilityCode1
Toward Adversarial Robustness via Semi-supervised Robust TrainingCode1
ARAE: Adversarially Robust Training of Autoencoders Improves Novelty DetectionCode1
Adversarial Machine Learning: Bayesian PerspectivesCode1
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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