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 1351–1375 of 1746 papers

Title	Date	Tasks	Status
Robust and differentially private stochastic linear bandits	Apr 23, 2023	Adversarial Robustness	—Unverified
Robust and Private Learning of Halfspaces	Nov 30, 2020	Adversarial Robustness	—Unverified
Adversarial Robustness for Unified Multi-Modal Encoders via Efficient Calibration	May 17, 2025	Adversarial Robustness	—Unverified
Adversarial Robustness for Tabular Data through Cost and Utility Awareness	Aug 27, 2022	Abuse DetectionAdversarial Robustness	—Unverified
RobustBlack: Challenging Black-Box Adversarial Attacks on State-of-the-Art Defenses	Dec 30, 2024	Adversarial Robustness	—Unverified
Robust Certification for Laplace Learning on Geometric Graphs	Apr 22, 2021	Adversarial AttackAdversarial Robustness	—Unverified
Adversarial Robustness for Machine Learning Cyber Defenses Using Log Data	Jul 29, 2020	Adversarial AttackAdversarial Robustness	—Unverified
Robust Collective Classification against Structural Attacks	Jul 26, 2020	Adversarial RobustnessClassification	—Unverified
Robust Dataset Distillation by Matching Adversarial Trajectories	Mar 15, 2025	Adversarial RobustnessDataset Distillation	—Unverified
Adversarial Robustness through Bias Variance Decomposition: A New Perspective for Federated Learning	Sep 18, 2020	Adversarial RobustnessFederated Learning	—Unverified
Robust Decentralized Learning with Local Updates and Gradient Tracking	May 2, 2024	Adversarial RobustnessEdge-computing	—Unverified
Robust Deep Learning Ensemble against Deception	Sep 14, 2020	Adversarial RobustnessDeep Learning	—Unverified
Adversarial robustness for latent models: Revisiting the robust-standard accuracies tradeoff	Oct 22, 2021	Adversarial RobustnessBinary Classification	—Unverified
Robust Distillation via Untargeted and Targeted Intermediate Adversarial Samples	Jan 1, 2024	Adversarial RobustnessKnowledge Distillation	—Unverified
RobustEdge: Low Power Adversarial Detection for Cloud-Edge Systems	Sep 5, 2023	Adversarial RobustnessQuantization	—Unverified
Robust Ensemble Model Training via Random Layer Sampling Against Adversarial Attack	May 21, 2020	Adversarial AttackAdversarial Robustness	—Unverified
Trace-Norm Adversarial Examples	Jul 2, 2020	Adversarial Robustness	—Unverified
Robust Explainability: A Tutorial on Gradient-Based Attribution Methods for Deep Neural Networks	Jul 23, 2021	Adversarial Robustness	—Unverified
Variational Autoencoders: A Harmonic Perspective	May 31, 2021	Adversarial RobustnessDecoder	—Unverified
Trading Inference-Time Compute for Adversarial Robustness	Jan 31, 2025	Adversarial Robustness	—Unverified
Robustified Domain Adaptation	Nov 18, 2020	Adversarial RobustnessDomain Adaptation	—Unverified
Robust Information Retrieval	Jun 13, 2024	Adversarial RobustnessInformation Retrieval	—Unverified
Your Classifier Can Do More: Towards Bridging the Gaps in Classification, Robustness, and Generation	May 26, 2025	Adversarial Robustness	—Unverified
Robust Linear Regression: Phase-Transitions and Precise Tradeoffs for General Norms	Aug 1, 2023	Adversarial Robustnessregression	—Unverified
Adversarial Robustness for Deep Learning-based Wildfire Prediction Models	Dec 28, 2024	Adversarial AttackAdversarial Robustness	—Unverified

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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