SOTAVerified

Adversarial Robustness

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

Papers

Showing 751800 of 1746 papers

TitleStatusHype
Revisiting Residual Networks for Adversarial Robustness: An Architectural PerspectiveCode1
In and Out-of-Domain Text Adversarial Robustness via Label Smoothing0
TextGrad: Advancing Robustness Evaluation in NLP by Gradient-Driven OptimizationCode1
Confidence-aware Training of Smoothed Classifiers for Certified RobustnessCode0
Estimating the Adversarial Robustness of Attributions in Text with Transformers0
On Evaluating Adversarial Robustness of Chest X-ray Classification: Pitfalls and Best Practices0
Understanding Zero-Shot Adversarial Robustness for Large-Scale ModelsCode1
AdvCat: Domain-Agnostic Robustness Assessment for Cybersecurity-Critical Applications with Categorical Inputs0
Adversarially Robust Video Perception by Seeing Motion0
Unfolding Local Growth Rate Estimates for (Almost) Perfect Adversarial DetectionCode0
Robust Perception through EquivarianceCode0
SRoUDA: Meta Self-training for Robust Unsupervised Domain AdaptationCode0
Robustness Implies Privacy in Statistical Estimation0
Enhancing Quantum Adversarial Robustness by Randomized Encodings0
CSTAR: Towards Compact and STructured Deep Neural Networks with Adversarial Robustness0
Recognizing Object by Components with Human Prior Knowledge Enhances Adversarial Robustness of Deep Neural NetworksCode0
Smoothing Policy Iteration for Zero-sum Markov Games0
Neural Representations Reveal Distinct Modes of Class Fitting in Residual Convolutional NetworksCode0
Generalizing and Improving Jacobian and Hessian Regularization0
Toward Robust Diagnosis: A Contour Attention Preserving Adversarial Defense for COVID-19 DetectionCode0
Quantization-aware Interval Bound Propagation for Training Certifiably Robust Quantized Neural NetworksCode0
Understanding the Impact of Adversarial Robustness on Accuracy DisparityCode0
Deep Learning Training Procedure Augmentations0
Towards Practical Control of Singular Values of Convolutional LayersCode0
Reliable Robustness Evaluation via Automatically Constructed Attack EnsemblesCode0
Improving Robust Generalization by Direct PAC-Bayesian Bound Minimization0
Enhancing Accuracy and Robustness of Steering Angle Prediction with Attention Mechanism0
Towards Adversarial Robustness of Deep Vision Algorithms0
Generalizable Deepfake Detection with Phase-Based Motion Analysis0
Improving Interpretability via Regularization of Neural Activation Sensitivity0
Differentially Private Optimizers Can Learn Adversarially Robust Models0
Improved techniques for deterministic l2 robustnessCode0
Demystify Transformers & Convolutions in Modern Image Deep NetworksCode1
Accelerating Adversarial Perturbation by 50% with Semi-backward Propagation0
Towards Adversarially Robust Recommendation from Adaptive Fraudster Detection0
Robust Lottery Tickets for Pre-trained Language ModelsCode1
An Adversarial Robustness Perspective on the Topology of Neural NetworksCode0
Improving Adversarial Robustness to Sensitivity and Invariance Attacks with Deep Metric Learning0
Data-free Defense of Black Box Models Against Adversarial AttacksCode0
Visually Adversarial Attacks and Defenses in the Physical World: A Survey0
Verifying And Interpreting Neural Networks using Finite AutomataCode0
Rethinking and Improving Robustness of Convolutional Neural Networks: a Shapley Value-based Approach in Frequency DomainCode1
Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative AttacksCode1
DensePure: Understanding Diffusion Models towards Adversarial Robustness0
Scoring Black-Box Models for Adversarial Robustness0
FI-ODE: Certifiably Robust Forward Invariance in Neural ODEsCode0
Improving Hyperspectral Adversarial Robustness Under Multiple Attacks0
Towards Reliable Neural Specifications0
Improving Adversarial Robustness with Self-Paced Hard-Class Pair ReweightingCode0
Disentangled Text Representation Learning with Information-Theoretic Perspective for Adversarial Robustness0
Show:102550
← PrevPage 16 of 35Next →

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