SOTAVerified

Adversarial Robustness

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

Papers

Showing 13011350 of 1746 papers

TitleStatusHype
Bayesian Inference with Certifiable Adversarial RobustnessCode0
Towards Bridging the gap between Empirical and Certified Robustness against Adversarial Examples0
Output Perturbation for Differentially Private Convex Optimization: Faster and More General0
SPADE: A Spectral Method for Black-Box Adversarial Robustness EvaluationCode0
Optimal Transport as a Defense Against Adversarial AttacksCode0
Adversarial Robustness Study of Convolutional Neural Network for Lumbar Disk Shape Reconstruction from MR imagesCode0
Learning Diverse-Structured Networks for Adversarial RobustnessCode0
Recent Advances in Adversarial Training for Adversarial Robustness0
Adversarial Learning with Cost-Sensitive Classes0
Error Diffusion Halftoning Against Adversarial ExamplesCode0
Exploring Adversarial Robustness of Multi-Sensor Perception Systems in Self Driving0
Mining Data Impressions from Deep Models as Substitute for the Unavailable Training Data0
Adversarially Robust and Explainable Model Compression with On-Device Personalization for Text Classification0
DiPSeN: Differentially Private Self-normalizing Neural Networks For Adversarial Robustness in Federated Learning0
The Effect of Prior Lipschitz Continuity on the Adversarial Robustness of Bayesian Neural Networks0
Adversarial Robustness by Design through Analog Computing and Synthetic GradientsCode0
Local Competition and Stochasticity for Adversarial Robustness in Deep Learning0
GridMix: Strong regularization through local context mapping0
Towards Robustness of Deep Neural Networks via Regularization0
Generative Max-Mahalanobis Classifiers for Image Classification, Generation and MoreCode0
Certifying Robustness of Graph Laplacian Based Semi-Supervised Learning0
Perceptual Adversarial Robustness: Generalizable Defenses Against Unforeseen Threat Models0
Disentangling Adversarial Robustness in Directions of the Data ManifoldCode0
How Benign is Benign Overfitting ?0
Buffer Zone based Defense against Adversarial Examples in Image Classification0
Manifold-aware Training: Increase Adversarial Robustness with Feature Clustering0
Intriguing class-wise properties of adversarial training0
Towards Adversarial Robustness of Bayesian Neural Network through Hierarchical Variational InferenceCode1
Perturbation Type Categorization for Multiple _p Bounded Adversarial Robustness0
Hierarchical Binding in Convolutional Neural Networks Confers Adversarial Robustness0
Test-Time Adaptation and Adversarial Robustness0
What are effective labels for augmented data? Improving robustness with AutoLabel0
Robust Multi-Agent Reinforcement Learning Driven by Correlated Equilibrium0
No Feature Is An Island: Adaptive Collaborations Between Features Improve Adversarial Robustness0
Collective Robustness Certificates0
Self-supervised Adversarial Robustness for the Low-label, High-data Regime0
Better Robustness by More Coverage: Adversarial Training with Mixup Augmentation for Robust Fine-tuningCode1
Improving Adversarial Robustness in Weight-quantized Neural Networks0
A Simple Fine-tuning Is All You Need: Towards Robust Deep Learning Via Adversarial Fine-tuning0
Gradient-Free Adversarial Attacks for Bayesian Neural NetworksCode0
Self-Progressing Robust TrainingCode0
Sample Complexity of Adversarially Robust Linear Classification on Separated Data0
On the human-recognizability phenomenon of adversarially trained deep image classifiersCode0
Adversarially Robust Estimate and Risk Analysis in Linear Regression0
Generate and Verify: Semantically Meaningful Formal Analysis of Neural Network Perception Systems0
A case for new neural network smoothness constraints0
Improving Adversarial Robustness via Probabilistically Compact Loss with Logit ConstraintsCode0
Achieving Adversarial Robustness Requires An Active Teacher0
Learning Energy-Based Models With Adversarial TrainingCode0
Composite Adversarial AttacksCode1
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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