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 1076–1100 of 1746 papers

Title	Date	Tasks	Status
Neural Belief Reasoner	Sep 10, 2019	Adversarial Robustness	—Unverified
Unlabeled Data Help: Minimax Analysis and Adversarial Robustness	Feb 14, 2022	Adversarial RobustnessSelf-Supervised Learning	—Unverified
A Hybrid Defense Strategy for Boosting Adversarial Robustness in Vision-Language Models	Oct 18, 2024	Adversarial RobustnessAutonomous Driving	—Unverified
Towards Adversarial Realism and Robust Learning for IoT Intrusion Detection and Classification	Jan 30, 2023	Adversarial RobustnessBinary Classification	—Unverified
Adversarial alignment: Breaking the trade-off between the strength of an attack and its relevance to human perception	Jun 5, 2023	Adversarial AttackAdversarial Robustness	—Unverified
A Holistic Assessment of the Reliability of Machine Learning Systems	Jul 20, 2023	Adversarial RobustnessOut-of-Distribution Detection	—Unverified
Towards Adversarial Robustness of Deep Vision Algorithms	Nov 19, 2022	Adversarial Robustnessimage-classification	—Unverified
New CleverHans Feature: Better Adversarial Robustness Evaluations with Attack Bundling	Nov 8, 2018	Adversarial Robustness	—Unverified
Fundamental Limits in Formal Verification of Message-Passing Neural Networks	Jun 10, 2022	Adversarial RobustnessClassification	—Unverified
A High Dimensional Statistical Model for Adversarial Training: Geometry and Trade-Offs	Feb 8, 2024	Adversarial Robustness	—Unverified
No Feature Is An Island: Adaptive Collaborations Between Features Improve Adversarial Robustness	Jan 1, 2021	Adversarial Robustnesscounterfactual	—Unverified
Unpacking Robustness in Inflectional Languages: Adversarial Evaluation and Mechanistic Insights	May 8, 2025	Adversarial Robustness	—Unverified
Non-Determinism in Neural Networks for Adversarial Robustness	May 26, 2019	Adversarial Robustness	—Unverified
Towards Adversarial Robustness via Transductive Learning	Jun 15, 2021	Adversarial RobustnessBilevel Optimization	—Unverified
Non-Singular Adversarial Robustness of Neural Networks	Feb 23, 2021	Adversarial RobustnessSensitivity	—Unverified
Towards Adversarial Robustness via Debiased High-Confidence Logit Alignment	Aug 12, 2024	Adversarial Robustness	—Unverified
A Geometrical Approach to Evaluate the Adversarial Robustness of Deep Neural Networks	Oct 10, 2023	Adversarial Robustness	—Unverified
A Fundamental Accuracy--Robustness Trade-off in Regression and Classification	Nov 6, 2024	Adversarial Robustnessregression	—Unverified
Unreasonable Effectiveness of Last Hidden Layer Activations for Adversarial Robustness	Feb 15, 2022	Adversarial Robustness	—Unverified
Unrevealed Threats: A Comprehensive Study of the Adversarial Robustness of Underwater Image Enhancement Models	Sep 10, 2024	Adversarial AttackAdversarial Robustness	—Unverified
Towards Assessment of Randomized Smoothing Mechanisms for Certifying Adversarial Robustness	May 15, 2020	Adversarial Robustness	—Unverified
On adversarial robustness and the use of Wasserstein ascent-descent dynamics to enforce it	Jan 9, 2023	Adversarial Robustness	—Unverified
On Adversarial Robustness of Deep Image Deblurring	Oct 5, 2022	Adversarial RobustnessDeblurring	—Unverified
On Adversarial Robustness of Language Models in Transfer Learning	Dec 29, 2024	Adversarial RobustnessTransfer Learning	—Unverified
On Adversarial Robustness of Small vs Large Batch Training	May 17, 2019	Adversarial 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