SOTAVerified

Adversarial Robustness

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

Papers

Showing 16511700 of 1746 papers

TitleStatusHype
Multiplicative Reweighting for Robust Neural Network OptimizationCode0
Transferable Adversarial Robustness for Categorical Data via Universal Robust EmbeddingsCode0
Confidence-aware Training of Smoothed Classifiers for Certified RobustnessCode0
The Ultimate Combo: Boosting Adversarial Example Transferability by Composing Data AugmentationsCode0
Confidence-aware Denoised Fine-tuning of Off-the-shelf Models for Certified RobustnessCode0
Improving Neural Network Robustness via Persistency of ExcitationCode0
The Uncanny Valley: Exploring Adversarial Robustness from a Flatness PerspectiveCode0
Approximate Manifold Defense Against Multiple Adversarial PerturbationsCode0
A PAC-Bayes Analysis of Adversarial RobustnessCode0
Adversarial Robustness in Multi-Task Learning: Promises and IllusionsCode0
Image Synthesis with a Single (Robust) ClassifierCode0
Annealing Self-Distillation Rectification Improves Adversarial TrainingCode0
A New Dataset Based on Images Taken by Blind People for Testing the Robustness of Image Classification Models Trained for ImageNet CategoriesCode0
Neural Fingerprints for Adversarial Attack DetectionCode0
Computational Asymmetries in Robust ClassificationCode0
Neural Population Geometry Reveals the Role of Stochasticity in Robust PerceptionCode0
Neural Representations Reveal Distinct Modes of Class Fitting in Residual Convolutional NetworksCode0
Squeeze Training for Adversarial RobustnessCode0
Neuro-Symbolic Verification of Deep Neural NetworksCode0
On Adversarial Robustness: A Neural Architecture Search perspectiveCode0
New Paradigm of Adversarial Training: Breaking Inherent Trade-Off between Accuracy and Robustness via Dummy ClassesCode0
Clustering Effect of (Linearized) Adversarial Robust ModelsCode0
A Deep Dive into Adversarial Robustness in Zero-Shot LearningCode0
NoiLIn: Improving Adversarial Training and Correcting Stereotype of Noisy LabelsCode0
Tighter Bounds on the Information Bottleneck with Application to Deep LearningCode0
Nonideality-aware training makes memristive networks more robust to adversarial attacksCode0
The interplay of robustness and generalization in quantum machine learningCode0
How many perturbations break this model? Evaluating robustness beyond adversarial accuracyCode0
Observations on K-image Expansion of Image-Mixing Augmentation for ClassificationCode0
Probing Predictions on OOD Images via Nearest CategoriesCode0
Omnipotent Adversarial Training in the WildCode0
On 1/n neural representation and robustnessCode0
On Adversarial Robustness and Out-of-Distribution Robustness of Large Language ModelsCode0
Robustness between the worst and average caseCode0
Adversarial Robustness Guarantees for Random Deep Neural NetworksCode0
Transferring Adversarial Robustness Through Robust Representation MatchingCode0
Robustness Implies Fairness in Causal Algorithmic RecourseCode0
Transformer Meets Twicing: Harnessing Unattended Residual InformationCode0
Enhancing Robustness in Incremental Learning with Adversarial TrainingCode0
On adversarial training and the 1 Nearest Neighbor classifierCode0
Topology Attack and Defense for Graph Neural Networks: An Optimization PerspectiveCode0
CertViT: Certified Robustness of Pre-Trained Vision TransformersCode0
Certifying Joint Adversarial Robustness for Model EnsemblesCode0
Certified Adversarial Robustness Within Multiple Perturbation BoundsCode0
An Empirical Study of Accuracy-Robustness Tradeoff and Training Efficiency in Self-Supervised LearningCode0
Center Smoothing: Certified Robustness for Networks with Structured OutputsCode0
Tougher Text, Smarter Models: Raising the Bar for Adversarial Defence BenchmarksCode0
On Evaluation of Adversarial Perturbations for Sequence-to-Sequence ModelsCode0
Using Pre-Training Can Improve Model Robustness and UncertaintyCode0
Adversarial Robustness Guarantees for Gaussian ProcessesCode0
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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