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 1376–1400 of 1746 papers

Title	Date	Tasks	Status	Hype
Contextual Fusion For Adversarial Robustness	Nov 18, 2020	Adversarial Robustness	—Unverified	0
Robustified Domain Adaptation	Nov 18, 2020	Adversarial RobustnessDomain Adaptation	—Unverified	0
SHIELD: Defending Textual Neural Networks against Multiple Black-Box Adversarial Attacks with Stochastic Multi-Expert Patcher	Nov 17, 2020	Adversarial Robustness	CodeCode Available	1
Probing Predictions on OOD Images via Nearest Categories	Nov 17, 2020	Adversarial RobustnessFew-Shot Learning	CodeCode Available	0
Towards Understanding the Regularization of Adversarial Robustness on Neural Networks	Nov 15, 2020	Adversarial Robustness	—Unverified	0
Adversarial Image Color Transformations in Explicit Color Filter Space	Nov 12, 2020	Adversarial Robustness	CodeCode Available	1
Bridging the Performance Gap between FGSM and PGD Adversarial Training	Nov 7, 2020	Adversarial AttackAdversarial Robustness	CodeCode Available	0
Recent Advances in Understanding Adversarial Robustness of Deep Neural Networks	Nov 3, 2020	Adversarial Robustness	—Unverified	0
Beyond cross-entropy: learning highly separable feature distributions for robust and accurate classification	Oct 29, 2020	Adversarial RobustnessGeneral Classification	—Unverified	0
Robust Pre-Training by Adversarial Contrastive Learning	Oct 26, 2020	Adversarial RobustnessContrastive Learning	CodeCode Available	1
Robustness May Be at Odds with Fairness: An Empirical Study on Class-wise Accuracy	Oct 26, 2020	Adversarial RobustnessAutonomous Driving	—Unverified	0
Towards Robust Neural Networks via Orthogonal Diversity	Oct 23, 2020	Adversarial RobustnessData Augmentation	CodeCode Available	0
Adversarial Robustness of Supervised Sparse Coding	Oct 22, 2020	Adversarial Robustness	CodeCode Available	0
On the Adversarial Robustness of LASSO Based Feature Selection	Oct 20, 2020	Adversarial Robustnessfeature selection	—Unverified	0
A case for new neural networks smoothness constraints	Oct 19, 2020	Adversarial RobustnessInductive Bias	—Unverified	0
RobustBench: a standardized adversarial robustness benchmark	Oct 19, 2020	Adversarial RobustnessBenchmarking	CodeCode Available	1
Optimism in the Face of Adversity: Understanding and Improving Deep Learning through Adversarial Robustness	Oct 19, 2020	Adversarial RobustnessDeep Learning	—Unverified	0
FADER: Fast Adversarial Example Rejection	Oct 18, 2020	Adversarial Robustness	—Unverified	0
Weight-Covariance Alignment for Adversarially Robust Neural Networks	Oct 17, 2020	Adversarial Robustness	CodeCode Available	0
An Analysis of Robustness of Non-Lipschitz Networks	Oct 13, 2020	Adversarial AttackAdversarial Defense	CodeCode Available	0
FaiR-N: Fair and Robust Neural Networks for Structured Data	Oct 13, 2020	Adversarial RobustnessAttribute	CodeCode Available	0
Shape-Texture Debiased Neural Network Training	Oct 12, 2020	Adversarial RobustnessData Augmentation	CodeCode Available	1
The Intrinsic Dimension of Images and Its Impact on Learning	Oct 10, 2020	Adversarial Robustness	—Unverified	0
Quantifying Adversarial Sensitivity of a Model as a Function of the Image Distribution	Oct 9, 2020	Adversarial RobustnessObject Recognition	—Unverified	0
Improve Adversarial Robustness via Weight Penalization on Classification Layer	Oct 8, 2020	Adversarial RobustnessClassification	—Unverified	0

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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