Adversarial Robustness

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

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1351–1375 of 1746 papers

Title	Date	Tasks	Status
Evaluating the Robustness of Geometry-Aware Instance-Reweighted Adversarial Training	Mar 2, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available
A Multiclass Boosting Framework for Achieving Fast and Provable Adversarial Robustness	Mar 1, 2021	Adversarial RobustnessObject Recognition	—Unverified
Explaining Adversarial Vulnerability with a Data Sparsity Hypothesis	Mar 1, 2021	Adversarial Robustness	CodeCode Available
Mind the box: l_1-APGD for sparse adversarial attacks on image classifiers	Mar 1, 2021	Adversarial Robustness	—Unverified
Adversarial Information Bottleneck	Feb 28, 2021	Adversarial Robustness	—Unverified
Towards Robust Graph Contrastive Learning	Feb 25, 2021	Adversarial RobustnessContrastive Learning	—Unverified
Adversarial Robustness with Non-uniform Perturbations	Feb 24, 2021	Adversarial RobustnessMalware Classification	CodeCode Available
Multiplicative Reweighting for Robust Neural Network Optimization	Feb 24, 2021	Adversarial Robustness	CodeCode Available
Non-Singular Adversarial Robustness of Neural Networks	Feb 23, 2021	Adversarial RobustnessSensitivity	—Unverified
The Effects of Image Distribution and Task on Adversarial Robustness	Feb 21, 2021	Adversarial RobustnessObject Recognition	—Unverified
A PAC-Bayes Analysis of Adversarial Robustness	Feb 19, 2021	Adversarial RobustnessGeneralization Bounds	CodeCode Available
Effective and Efficient Vote Attack on Capsule Networks	Feb 19, 2021	Adversarial Robustness	CodeCode Available
Center Smoothing: Certified Robustness for Networks with Structured Outputs	Feb 19, 2021	Adversarial RobustnessDimensionality Reduction	CodeCode Available
Random Projections for Improved Adversarial Robustness	Feb 18, 2021	Adversarial RobustnessDimensionality Reduction	—Unverified
Improving Hierarchical Adversarial Robustness of Deep Neural Networks	Feb 17, 2021	Adversarial DefenseAdversarial Robustness	—Unverified
Bridging the Gap Between Adversarial Robustness and Optimization Bias	Feb 17, 2021	Adversarial Robustness	CodeCode Available
Generating Structured Adversarial Attacks Using Frank-Wolfe Method	Feb 15, 2021	Adversarial Robustness	—Unverified
Data Quality Matters For Adversarial Training: An Empirical Study	Feb 15, 2021	Adversarial Robustness	CodeCode Available
Guided Interpolation for Adversarial Training	Feb 15, 2021	Adversarial Robustness	—Unverified
And/or trade-off in artificial neurons: impact on adversarial robustness	Feb 15, 2021	Adversarial RobustnessBIG-bench Machine Learning	—Unverified
CAP-GAN: Towards Adversarial Robustness with Cycle-consistent Attentional Purification	Feb 15, 2021	Adversarial AttackAdversarial Robustness	—Unverified
Exploring Adversarial Robustness of Deep Metric Learning	Feb 14, 2021	Adversarial RobustnessMetric Learning	CodeCode Available
Bayesian Inference with Certifiable Adversarial Robustness	Feb 10, 2021	Adversarial RobustnessBayesian Inference	CodeCode Available
Adversarial Robustness: What fools you makes you stronger	Feb 10, 2021	Adversarial RobustnessPAC learning	—Unverified
Towards Bridging the gap between Empirical and Certified Robustness against Adversarial Examples	Feb 9, 2021	Adversarial Robustness	—Unverified

Show:10 25 50

← PrevPage 55 of 70Next →

All datasets AdvGLUE CIFAR-10 ImageNet ImageNet-A ImageNet-C Stylized ImageNet CIFAR-100

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	DeBERTa (single model)	Accuracy	0.61	—	Unverified
2	ALBERT (single model)	Accuracy	0.59	—	Unverified
3	T5 (single model)	Accuracy	0.57	—	Unverified
4	SMART_RoBERTa (single model)	Accuracy	0.54	—	Unverified
5	FreeLB (single model)	Accuracy	0.5	—	Unverified
6	RoBERTa (single model)	Accuracy	0.5	—	Unverified
7	InfoBERT (single model)	Accuracy	0.46	—	Unverified
8	ELECTRA (single model)	Accuracy	0.42	—	Unverified
9	BERT (single model)	Accuracy	0.34	—	Unverified
10	SMART_BERT (single model)	Accuracy	0.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Mixed classifier	Accuracy	95.23	—	Unverified
2	Stochastic-LWTA/PGD/WideResNet-34-10	Accuracy	92.26	—	Unverified
3	Stochastic-LWTA/PGD/WideResNet-34-5	Accuracy	91.88	—	Unverified
4	GLOT-DR	Accuracy	84.13	—	Unverified
5	TRADES-ANCRA/ResNet18	Accuracy	81.7	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ResNet-50 (SGD, Cosine)	Accuracy	77.4	—	Unverified
2	ResNet-50 (SGD, Step)	Accuracy	76.9	—	Unverified
3	DeiT-S (AdamW, Cosine)	Accuracy	76.8	—	Unverified
4	ResNet-50 (AdamW, Cosine)	Accuracy	76.4	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DeiT-S (AdamW, Cosine)	Accuracy	12.2	—	Unverified
2	ResNet-50 (SGD, Cosine)	Accuracy	3.3	—	Unverified
3	ResNet-50 (SGD, Step)	Accuracy	3.2	—	Unverified
4	ResNet-50 (AdamW, Cosine)	Accuracy	3.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ResNet-50 (AdamW, Cosine)	mean Corruption Error (mCE)	59.3	—	Unverified
2	ResNet-50 (SGD, Step)	mean Corruption Error (mCE)	57.9	—	Unverified
3	ResNet-50 (SGD, Cosine)	mean Corruption Error (mCE)	56.9	—	Unverified
4	DeiT-S (AdamW, Cosine)	mean Corruption Error (mCE)	48	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DeiT-S (AdamW, Cosine)	Accuracy	13	—	Unverified
2	ResNet-50 (SGD, Cosine)	Accuracy	8.4	—	Unverified
3	ResNet-50 (SGD, Step)	Accuracy	8.3	—	Unverified
4	ResNet-50 (AdamW, Cosine)	Accuracy	8.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Mixed Classifier	Clean Accuracy	85.21	—	Unverified
2	ResNet18/MART-ANCRA	Clean Accuracy	60.1	—	Unverified