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 1111–1120 of 1746 papers

Title	Date	Tasks	Status	Hype
Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networks	Jul 27, 2022	Adversarial RobustnessBenchmarking	—Unverified	0
Trace-Norm Adversarial Examples	Jul 2, 2020	Adversarial Robustness	—Unverified	0
Trading Inference-Time Compute for Adversarial Robustness	Jan 31, 2025	Adversarial Robustness	—Unverified	0
Training Robust Deep Neural Networks via Adversarial Noise Propagation	Sep 19, 2019	Adversarial DefenseAdversarial Robustness	—Unverified	0
Training Safe Neural Networks with Global SDP Bounds	Sep 15, 2024	Adversarial Robustness	—Unverified	0
Towards Model-Agnostic Adversarial Defenses using Adversarially Trained Autoencoders	Sep 12, 2019	Adversarial DefenseAdversarial Robustness	—Unverified	0
Transfer of Adversarial Robustness Between Perturbation Types	May 3, 2019	Adversarial Robustness	—Unverified	0
Transgressing the boundaries: towards a rigorous understanding of deep learning and its (non-)robustness	Jul 5, 2023	Adversarial RobustnessLearning Theory	—Unverified	0
Two Heads are Better than One: Towards Better Adversarial Robustness by Combining Transduction and Rejection	May 27, 2023	Adversarial Robustness	—Unverified	0
Two is Better than One: Efficient Ensemble Defense for Robust and Compact Models	Apr 7, 2025	Adversarial RobustnessDiversity	—Unverified	0

Show:10 25 50

← PrevPage 112 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