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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 1491–1500 of 1746 papers

Title	Date	Tasks	Status	Hype	Score
Scalable Quantitative Verification For Deep Neural Networks	Feb 17, 2020	Adversarial Robustness	—Unverified	0	0
Demotivate adversarial defense in remote sensing	May 28, 2021	Adversarial DefenseAdversarial Robustness	—Unverified	0	0
Catastrophic Overfitting: A Potential Blessing in Disguise	Feb 28, 2024	Adversarial Robustness	—Unverified	0	0
CARE: Ensemble Adversarial Robustness Evaluation Against Adaptive Attackers for Security Applications	Jan 20, 2024	Adversarial Robustness	—Unverified	0	0
Scalable Whitebox Attacks on Tree-based Models	Mar 31, 2022	Adversarial Robustness	—Unverified	0	0
CAP-GAN: Towards Adversarial Robustness with Cycle-consistent Attentional Purification	Feb 15, 2021	Adversarial AttackAdversarial Robustness	—Unverified	0	0
Denoising Autoencoder-based Defensive Distillation as an Adversarial Robustness Algorithm	Mar 28, 2023	Adversarial RobustnessData Poisoning	—Unverified	0	0
Visual Interpretability Alone Helps Adversarial Robustness	Sep 25, 2019	Adversarial Robustness	—Unverified	0	0
DensePure: Understanding Diffusion Models towards Adversarial Robustness	Nov 1, 2022	Adversarial RobustnessDenoising	—Unverified	0	0
Adversarially Robust Spiking Neural Networks with Sparse Connectivity	May 16, 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