SOTAVerified|Agents Browse Leaderboard About

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 1411–1420 of 1746 papers

Title	Date	Tasks	Status	Hype
How to compare adversarial robustness of classifiers from a global perspective	Apr 22, 2020	Adversarial RobustnessBIG-bench Machine Learning	CodeCode Available	0
Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks	Nov 20, 2024	Adversarial Robustnessimage-classification	CodeCode Available	0
Error Diffusion Halftoning Against Adversarial Examples	Jan 23, 2021	Adversarial RobustnessQuantization	CodeCode Available	0
ProARD: progressive adversarial robustness distillation: provide wide range of robust students	Jun 9, 2025	Adversarial Robustness	CodeCode Available	0
Error Correcting Output Codes Improve Probability Estimation and Adversarial Robustness of Deep Neural Networks	Dec 1, 2019	Adversarial DefenseAdversarial Robustness	CodeCode Available	0
Enhancing Multiple Reliability Measures via Nuisance-extended Information Bottleneck	Mar 24, 2023	Adversarial RobustnessNovelty Detection	CodeCode Available	0
Beyond Pretrained Features: Noisy Image Modeling Provides Adversarial Defense	Feb 2, 2023	Adversarial DefenseAdversarial Robustness	CodeCode Available	0
Adversarial Machine Learning in Latent Representations of Neural Networks	Sep 29, 2023	Adversarial RobustnessEdge-computing	CodeCode Available	0
Enhancing Adversarial Training via Reweighting Optimization Trajectory	Jun 25, 2023	Adversarial Robustness	CodeCode Available	0
Adversarial Robustness via Fisher-Rao Regularization	Jun 12, 2021	Adversarial DefenseAdversarial Robustness	CodeCode Available	0

Show:10 25 50

← PrevPage 142 of 175Next →

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