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 461–470 of 1746 papers

Title	Date	Tasks	Status	Hype	Score
Enhancing Robustness in Incremental Learning with Adversarial Training	Dec 6, 2023	Adversarial Robustnessclass-incremental learning	CodeCode Available	0	5
Adversarial Robustness of Supervised Sparse Coding	Oct 22, 2020	Adversarial Robustness	CodeCode Available	0	5
Fake It Until You Break It: On the Adversarial Robustness of AI-generated Image Detectors	Oct 2, 2024	Adversarial RobustnessMisinformation	CodeCode Available	0	5
Benchmarking Robust Self-Supervised Learning Across Diverse Downstream Tasks	Jul 17, 2024	Adversarial RobustnessBenchmarking	CodeCode Available	0	5
Clustering Effect of (Linearized) Adversarial Robust Models	Nov 25, 2021	Adversarial RobustnessClustering	CodeCode Available	0	5
Squeeze Training for Adversarial Robustness	May 23, 2022	Adversarial Robustness	CodeCode Available	0	5
Fast Adversarial Robustness Certification of Nearest Prototype Classifiers for Arbitrary Seminorms	Dec 1, 2020	Adversarial RobustnessQuantization	CodeCode Available	0	5
Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack	Jun 15, 2022	Adversarial RobustnessComputational Efficiency	CodeCode Available	0	5
Benchmarking Adversarial Robustness to Bias Elicitation in Large Language Models: Scalable Automated Assessment with LLM-as-a-Judge	Apr 10, 2025	Adversarial RobustnessBenchmarking	CodeCode Available	0	5
Adversarial Robustness of Stabilized NeuralODEs Might be from Obfuscated Gradients	Sep 28, 2020	Adversarial DefenseAdversarial Robustness	CodeCode Available	0	5

Show:10 25 50

← PrevPage 47 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