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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 961–970 of 1746 papers

Title	Date	Tasks	Status	Hype
Q-TART: Quickly Training for Adversarial Robustness and in-Transferability	Apr 14, 2022	Adversarial Robustness	—Unverified	0
From Environmental Sound Representation to Robustness of 2D CNN Models Against Adversarial Attacks	Apr 14, 2022	Adversarial AttackAdversarial Robustness	—Unverified	0
A Simple Approach to Adversarial Robustness in Few-shot Image Classification	Apr 11, 2022	Adversarial RobustnessFew-Shot Image Classification	CodeCode Available	0
Evaluating the Adversarial Robustness for Fourier Neural Operators	Apr 8, 2022	Adversarial Robustnessscientific discovery	—Unverified	0
Masking Adversarial Damage: Finding Adversarial Saliency for Robust and Sparse Network	Apr 6, 2022	Adversarial RobustnessModel Compression	CodeCode Available	1
Distilling Robust and Non-Robust Features in Adversarial Examples by Information Bottleneck	Apr 6, 2022	Adversarial Robustness	CodeCode Available	1
Adversarial Robustness through the Lens of Convolutional Filters	Apr 5, 2022	Adversarial Robustness	CodeCode Available	0
Hear No Evil: Towards Adversarial Robustness of Automatic Speech Recognition via Multi-Task Learning	Apr 5, 2022	Adversarial AttackAdversarial Robustness	—Unverified	0
SkeleVision: Towards Adversarial Resiliency of Person Tracking with Multi-Task Learning	Apr 2, 2022	Adversarial RobustnessAutonomous Driving	CodeCode Available	0
Scalable Whitebox Attacks on Tree-based Models	Mar 31, 2022	Adversarial Robustness	—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