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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 1381–1390 of 1746 papers

Title	Date	Tasks	Status	Hype
Gradient-Free Adversarial Attacks for Bayesian Neural Networks	Dec 23, 2020	Adversarial RobustnessBayesian Inference	CodeCode Available	0
Evaluating the Adversarial Robustness of Semantic Segmentation: Trying Harder Pays Off	Jul 12, 2024	Adversarial Robustnessimage-classification	CodeCode Available	0
GREAT Score: Global Robustness Evaluation of Adversarial Perturbation using Generative Models	Apr 19, 2023	Adversarial Robustness	CodeCode Available	0
Adversarial Robustness with Non-uniform Perturbations	Feb 24, 2021	Adversarial RobustnessMalware Classification	CodeCode Available	0
Adversarial Neural Pruning with Latent Vulnerability Suppression	Aug 12, 2019	Adversarial Robustness	CodeCode Available	0
Boosting Adversarial Training via Fisher-Rao Norm-based Regularization	Mar 26, 2024	Adversarial Robustness	CodeCode Available	0
SkeleVision: Towards Adversarial Resiliency of Person Tracking with Multi-Task Learning	Apr 2, 2022	Adversarial RobustnessAutonomous Driving	CodeCode Available	0
Adversarial Robustness vs. Model Compression, or Both?	Oct 1, 2019	Adversarial Robustnessmodel	CodeCode Available	0
Hardening DNNs against Transfer Attacks during Network Compression using Greedy Adversarial Pruning	Jun 15, 2022	Adversarial RobustnessQuantization	CodeCode Available	0
PointACL:Adversarial Contrastive Learning for Robust Point Clouds Representation under Adversarial Attack	Sep 14, 2022	3D ClassificationAdversarial Attack	CodeCode Available	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