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 951–975 of 1746 papers

Title	Date	Tasks	Status
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
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
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
Robust Explainability: A Tutorial on Gradient-Based Attribution Methods for Deep Neural Networks	Jul 23, 2021	Adversarial Robustness	—Unverified
Robustified Domain Adaptation	Nov 18, 2020	Adversarial RobustnessDomain Adaptation	—Unverified
Robust Information Retrieval	Jun 13, 2024	Adversarial RobustnessInformation Retrieval	—Unverified
Robust Linear Regression: Phase-Transitions and Precise Tradeoffs for General Norms	Aug 1, 2023	Adversarial Robustnessregression	—Unverified
Robust low-rank training via approximate orthonormal constraints	Jun 2, 2023	Adversarial Robustness	—Unverified
RobustMQ: Benchmarking Robustness of Quantized Models	Aug 4, 2023	Adversarial RobustnessBenchmarking	—Unverified
Robust Multi-Agent Reinforcement Learning Driven by Correlated Equilibrium	Jan 1, 2021	Adversarial RobustnessMulti-agent Reinforcement Learning	—Unverified
Robustness Against Adversarial Attacks via Learning Confined Adversarial Polytopes	Jan 15, 2024	Adversarial Robustness	—Unverified
Robustness Certificates for Implicit Neural Networks: A Mixed Monotone Contractive Approach	Dec 10, 2021	Adversarial Robustness	—Unverified
Robustness-Congruent Adversarial Training for Secure Machine Learning Model Updates	Feb 27, 2024	Adversarial Robustnessregression	—Unverified
Robustness Implies Privacy in Statistical Estimation	Dec 9, 2022	Adversarial Robustnessparameter estimation	—Unverified
A Systematic Review of Robustness in Deep Learning for Computer Vision: Mind the gap?	Dec 1, 2021	Adversarial RobustnessData Augmentation	—Unverified
Robustness May Be at Odds with Fairness: An Empirical Study on Class-wise Accuracy	Oct 26, 2020	Adversarial RobustnessAutonomous Driving	—Unverified
Robustness of deep learning classification to adversarial input on GPUs: asynchronous parallel accumulation is a source of vulnerability	Mar 21, 2025	Adversarial RobustnessBayesian Optimization	—Unverified
Robustness of Explanation Methods for NLP Models	Jun 24, 2022	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