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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 1161–1170 of 1746 papers

Title	Date	Tasks	Status	Hype	Score
Advancing Adversarial Robustness Through Adversarial Logit Update	Aug 29, 2023	Adversarial PurificationAdversarial Robustness	—Unverified	0	0
AdPO: Enhancing the Adversarial Robustness of Large Vision-Language Models with Preference Optimization	Apr 2, 2025	Adversarial DefenseAdversarial Robustness	—Unverified	0	0
On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms	Feb 13, 2025	Adversarial Robustness	—Unverified	0	0
Adversarial Robustness without Adversarial Training: A Teacher-Guided Curriculum Learning Approach	Dec 1, 2021	Adversarial Robustness	—Unverified	0	0
Adversarial Robustness: What fools you makes you stronger	Feb 10, 2021	Adversarial RobustnessPAC learning	—Unverified	0	0
On the Relationship Between Adversarial Robustness and Decision Region in Deep Neural Network	Jul 7, 2022	Adversarial AttackAdversarial Robustness	—Unverified	0	0
Unveiling the Role of Randomization in Multiclass Adversarial Classification: Insights from Graph Theory	Mar 18, 2025	Adversarial RobustnessBinary Classification	—Unverified	0	0
On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective	Feb 22, 2023	Adversarial RobustnessChatbot	—Unverified	0	0
On the Robustness of Kolmogorov-Arnold Networks: An Adversarial Perspective	Aug 25, 2024	Adversarial Robustnessimage-classification	—Unverified	0	0
On the Robustness Tradeoff in Fine-Tuning	Mar 19, 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