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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 921–930 of 1746 papers

Title	Date	Tasks	Status	Hype	Score
Are Adversarial Robustness and Common Perturbation Robustness Independent Attributes ?	Sep 4, 2019	Adversarial Robustness	—Unverified	0	0
Improving Robustness with Image Filtering	Dec 21, 2021	Adversarial RobustnessData Augmentation	—Unverified	0	0
Architectural Adversarial Robustness: The Case for Deep Pursuit	Nov 29, 2020	Adversarial Robustnessglobal-optimization	—Unverified	0	0
The Effect of Prior Lipschitz Continuity on the Adversarial Robustness of Bayesian Neural Networks	Jan 7, 2021	Adversarial RobustnessVariational Inference	—Unverified	0	0
ARBiBench: Benchmarking Adversarial Robustness of Binarized Neural Networks	Dec 21, 2023	Adversarial RobustnessBenchmarking	—Unverified	0	0
The Effects of Image Distribution and Task on Adversarial Robustness	Feb 21, 2021	Adversarial RobustnessObject Recognition	—Unverified	0	0
Improving Calibration through the Relationship with Adversarial Robustness	Jun 29, 2020	Adversarial Robustness	—Unverified	0	0
Improving White-box Robustness of Pre-processing Defenses via Joint Adversarial Training	Jun 10, 2021	Adversarial DefenseAdversarial Robustness	—Unverified	0	0
In and Out-of-Domain Text Adversarial Robustness via Label Smoothing	Dec 20, 2022	Adversarial Robustness	—Unverified	0	0
The Importance of Robust Features in Mitigating Catastrophic Forgetting	Jun 29, 2023	Adversarial RobustnessContinual Learning	—Unverified	0	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