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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 801–810 of 1746 papers

Title	Date	Tasks	Status	Hype	Score
A Comparative Analysis of Adversarial Robustness for Quantum and Classical Machine Learning Models	Apr 24, 2024	Adversarial RobustnessQuantum Machine Learning	—Unverified	0	0
Curvature Dynamic Black-box Attack: revisiting adversarial robustness via dynamic curvature estimation	May 25, 2025	Adversarial AttackAdversarial Robustness	—Unverified	0	0
A More Biologically Plausible Local Learning Rule for ANNs	Nov 24, 2020	Adversarial RobustnessBinary Classification	—Unverified	0	0
CSTAR: Towards Compact and STructured Deep Neural Networks with Adversarial Robustness	Dec 4, 2022	Adversarial RobustnessModel Compression	—Unverified	0	0
Cross-Entropy Loss Functions: Theoretical Analysis and Applications	Apr 14, 2023	Adversarial Robustness	—Unverified	0	0
A margin-based replacement for cross-entropy loss	Jan 21, 2025	Adversarial RobustnessContinual Learning	—Unverified	0	0
Cross Domain Generative Augmentation: Domain Generalization with Latent Diffusion Models	Dec 8, 2023	Adversarial RobustnessData Augmentation	—Unverified	0	0
Criticality Leveraged Adversarial Training (CLAT) for Boosted Performance via Parameter Efficiency	Aug 19, 2024	Adversarial Robustness	—Unverified	0	0
Corruption-Robust Offline Reinforcement Learning	Jun 11, 2021	Adversarial RobustnessOffline RL	—Unverified	0	0
ALMA: Aggregated Lipschitz Maximization Attack on Auto-encoders	May 6, 2025	Adversarial Robustness	—Unverified	0	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