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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 121–130 of 1746 papers

Title	Date	Tasks	Status	Hype
Adversarial Robustness in Two-Stage Learning-to-Defer: Algorithms and Guarantees	Feb 3, 2025	Adversarial Robustness	—Unverified	0
SecPE: Secure Prompt Ensembling for Private and Robust Large Language Models	Feb 2, 2025	Adversarial RobustnessPrivacy Preserving	—Unverified	0
Boosting Adversarial Robustness and Generalization with Structural Prior	Feb 2, 2025	Adversarial RobustnessDeep Learning	—Unverified	0
Trading Inference-Time Compute for Adversarial Robustness	Jan 31, 2025	Adversarial Robustness	—Unverified	0
CAMP in the Odyssey: Provably Robust Reinforcement Learning with Certified Radius Maximization	Jan 29, 2025	Adversarial RobustnessDeep Reinforcement Learning	CodeCode Available	0
Topological Signatures of Adversaries in Multimodal Alignments	Jan 29, 2025	Adversarial Robustness	—Unverified	0
Adversarial Robustness of Partitioned Quantum Classifiers	Jan 28, 2025	Adversarial RobustnessQuantum Machine Learning	—Unverified	0
Adversarial Masked Autoencoder Purifier with Defense Transferability	Jan 28, 2025	Adversarial DefenseAdversarial Robustness	—Unverified	0
Adversarially Robust Bloom Filters: Privacy, Reductions, and Open Problems	Jan 27, 2025	Adversarial Robustnessmodel	—Unverified	0
Pre-trained Model Guided Mixture Knowledge Distillation for Adversarial Federated Learning	Jan 25, 2025	Adversarial RobustnessFederated Learning	—Unverified	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