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Adversarial Robustness

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

Papers

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Showing 1351–1400 of 1746 papers

TitleStatusHype
Evaluating the Robustness of Geometry-Aware Instance-Reweighted Adversarial TrainingCode0
A Multiclass Boosting Framework for Achieving Fast and Provable Adversarial Robustness—0
Explaining Adversarial Vulnerability with a Data Sparsity HypothesisCode0
Mind the box: l_1-APGD for sparse adversarial attacks on image classifiers—0
Adversarial Information Bottleneck—0
Towards Robust Graph Contrastive Learning—0
Adversarial Robustness with Non-uniform PerturbationsCode0
Multiplicative Reweighting for Robust Neural Network OptimizationCode0
Non-Singular Adversarial Robustness of Neural Networks—0
The Effects of Image Distribution and Task on Adversarial Robustness—0
A PAC-Bayes Analysis of Adversarial RobustnessCode0
Effective and Efficient Vote Attack on Capsule NetworksCode0
Center Smoothing: Certified Robustness for Networks with Structured OutputsCode0
Random Projections for Improved Adversarial Robustness—0
Improving Hierarchical Adversarial Robustness of Deep Neural Networks—0
Bridging the Gap Between Adversarial Robustness and Optimization BiasCode0
Generating Structured Adversarial Attacks Using Frank-Wolfe Method—0
Data Quality Matters For Adversarial Training: An Empirical StudyCode0
Guided Interpolation for Adversarial Training—0
And/or trade-off in artificial neurons: impact on adversarial robustness—0
CAP-GAN: Towards Adversarial Robustness with Cycle-consistent Attentional Purification—0
Exploring Adversarial Robustness of Deep Metric LearningCode0
Bayesian Inference with Certifiable Adversarial RobustnessCode0
Adversarial Robustness: What fools you makes you stronger—0
Towards Bridging the gap between Empirical and Certified Robustness against Adversarial Examples—0
Output Perturbation for Differentially Private Convex Optimization: Faster and More General—0
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 Robustness—0
Adversarial Learning with Cost-Sensitive Classes—0
Error Diffusion Halftoning Against Adversarial ExamplesCode0
Exploring Adversarial Robustness of Multi-Sensor Perception Systems in Self Driving—0
Mining Data Impressions from Deep Models as Substitute for the Unavailable Training Data—0
Adversarially Robust and Explainable Model Compression with On-Device Personalization for Text Classification—0
DiPSeN: Differentially Private Self-normalizing Neural Networks For Adversarial Robustness in Federated Learning—0
The Effect of Prior Lipschitz Continuity on the Adversarial Robustness of Bayesian Neural Networks—0
Adversarial Robustness by Design through Analog Computing and Synthetic GradientsCode0
Local Competition and Stochasticity for Adversarial Robustness in Deep Learning—0
Perceptual Adversarial Robustness: Generalizable Defenses Against Unforeseen Threat Models—0
How Benign is Benign Overfitting ?—0
Certifying Robustness of Graph Laplacian Based Semi-Supervised Learning—0
Manifold-aware Training: Increase Adversarial Robustness with Feature Clustering—0
Towards Robustness of Deep Neural Networks via Regularization—0
Hierarchical Binding in Convolutional Neural Networks Confers Adversarial Robustness—0
Self-supervised Adversarial Robustness for the Low-label, High-data Regime—0
GridMix: Strong regularization through local context mapping—0
Robust Multi-Agent Reinforcement Learning Driven by Correlated Equilibrium—0
Generative Max-Mahalanobis Classifiers for Image Classification, Generation and MoreCode0
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All datasetsAdvGLUECIFAR-10ImageNetImageNet-AImageNet-CStylized ImageNetCIFAR-100

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeBERTa (single model)Accuracy0.61—Unverified
2ALBERT (single model)Accuracy0.59—Unverified
3T5 (single model)Accuracy0.57—Unverified
4SMART_RoBERTa (single model)Accuracy0.54—Unverified
5FreeLB (single model)Accuracy0.5—Unverified
6RoBERTa (single model)Accuracy0.5—Unverified
7InfoBERT (single model)Accuracy0.46—Unverified
8ELECTRA (single model)Accuracy0.42—Unverified
9BERT (single model)Accuracy0.34—Unverified
10SMART_BERT (single model)Accuracy0.3—Unverified
#ModelMetricClaimedVerifiedStatus
1Mixed classifierAccuracy95.23—Unverified
2Stochastic-LWTA/PGD/WideResNet-34-10Accuracy92.26—Unverified
3Stochastic-LWTA/PGD/WideResNet-34-5Accuracy91.88—Unverified
4GLOT-DRAccuracy84.13—Unverified
5TRADES-ANCRA/ResNet18Accuracy81.7—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet-50 (SGD, Cosine)Accuracy77.4—Unverified
2ResNet-50 (SGD, Step)Accuracy76.9—Unverified
3DeiT-S (AdamW, Cosine)Accuracy76.8—Unverified
4ResNet-50 (AdamW, Cosine)Accuracy76.4—Unverified
#ModelMetricClaimedVerifiedStatus
1DeiT-S (AdamW, Cosine)Accuracy12.2—Unverified
2ResNet-50 (SGD, Cosine)Accuracy3.3—Unverified
3ResNet-50 (SGD, Step)Accuracy3.2—Unverified
4ResNet-50 (AdamW, Cosine)Accuracy3.1—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet-50 (AdamW, Cosine)mean Corruption Error (mCE)59.3—Unverified
2ResNet-50 (SGD, Step)mean Corruption Error (mCE)57.9—Unverified
3ResNet-50 (SGD, Cosine)mean Corruption Error (mCE)56.9—Unverified
4DeiT-S (AdamW, Cosine)mean Corruption Error (mCE)48—Unverified
#ModelMetricClaimedVerifiedStatus
1DeiT-S (AdamW, Cosine)Accuracy13—Unverified
2ResNet-50 (SGD, Cosine)Accuracy8.4—Unverified
3ResNet-50 (SGD, Step)Accuracy8.3—Unverified
4ResNet-50 (AdamW, Cosine)Accuracy8.1—Unverified
#ModelMetricClaimedVerifiedStatus
1Mixed ClassifierClean Accuracy85.21—Unverified
2ResNet18/MART-ANCRAClean Accuracy60.1—Unverified