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 301–325 of 1746 papers

Title	Date	Tasks	Status	Hype
Adversarial Vertex Mixup: Toward Better Adversarially Robust Generalization	Mar 5, 2020	Adversarial RobustnessData Augmentation	CodeCode Available	1
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks	Mar 3, 2020	Adversarial Robustness	CodeCode Available	1
Learn2Perturb: an End-to-end Feature Perturbation Learning to Improve Adversarial Robustness	Mar 2, 2020	Adversarial DefenseAdversarial Robustness	CodeCode Available	1
Attacks Which Do Not Kill Training Make Adversarial Learning Stronger	Feb 26, 2020	Adversarial Robustness	CodeCode Available	1
Learning Adversarially Robust Representations via Worst-Case Mutual Information Maximization	Feb 26, 2020	Adversarial Robustness	CodeCode Available	1
Hold me tight! Influence of discriminative features on deep network boundaries	Feb 15, 2020	Adversarial Robustness	CodeCode Available	1
Adversarial Robustness for Code	Feb 11, 2020	Adversarial RobustnessBIG-bench Machine Learning	CodeCode Available	1
Random Smoothing Might be Unable to Certify _ Robustness for High-Dimensional Images	Feb 10, 2020	Adversarial Robustness	CodeCode Available	1
Renofeation: A Simple Transfer Learning Method for Improved Adversarial Robustness	Feb 7, 2020	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Towards Sharper First-Order Adversary with Quantized Gradients	Feb 1, 2020	Adversarial RobustnessQuantization	CodeCode Available	1
Adversarial Robustness Against the Union of Multiple Threat Models	Jan 1, 2020	Adversarial Robustness	CodeCode Available	1
Explainability and Adversarial Robustness for RNNs	Dec 20, 2019	Adversarial RobustnessFeature Importance	CodeCode Available	1
Universal Adversarial Robustness of Texture and Shape-Biased Models	Nov 23, 2019	Adversarial RobustnessImage Classification	CodeCode Available	1
Adversarial Robustness Against the Union of Multiple Perturbation Models	Sep 9, 2019	Adversarial Robustness	CodeCode Available	1
MNIST-C: A Robustness Benchmark for Computer Vision	Jun 5, 2019	Adversarial RobustnessBenchmarking	CodeCode Available	1
Adversarial Robustness as a Prior for Learned Representations	Jun 3, 2019	Adversarial Robustness	CodeCode Available	1
Adversarially Robust Distillation	May 23, 2019	Adversarial RobustnessKnowledge Distillation	CodeCode Available	1
Wasserstein Adversarial Examples via Projected Sinkhorn Iterations	Feb 21, 2019	Adversarial AttackAdversarial Defense	CodeCode Available	1
On Evaluating Adversarial Robustness	Feb 18, 2019	Adversarial AttackAdversarial Defense	CodeCode Available	1
Certified Adversarial Robustness via Randomized Smoothing	Feb 8, 2019	Adversarial DefenseAdversarial Robustness	CodeCode Available	1
Improving Adversarial Robustness via Promoting Ensemble Diversity	Jan 25, 2019	Adversarial RobustnessDiversity	CodeCode Available	1
Theoretically Principled Trade-off between Robustness and Accuracy	Jan 24, 2019	Adversarial AttackAdversarial Defense	CodeCode Available	1
Robustness May Be at Odds with Accuracy	May 30, 2018	Adversarial Robustness	CodeCode Available	1
Towards Deep Learning Models Resistant to Adversarial Attacks	Jun 19, 2017	Adversarial AttackAdversarial Defense	CodeCode Available	1
Bridging Robustness and Generalization Against Word Substitution Attacks in NLP via the Growth Bound Matrix Approach	Jul 14, 2025	Adversarial DefenseAdversarial Robustness	CodeCode Available	0

Show:10 25 50

← PrevPage 13 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