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 1151–1175 of 1746 papers

Title	Date	Tasks	Status
Vulnerabilities in AI-generated Image Detection: The Challenge of Adversarial Attacks	Jul 30, 2024	Adversarial AttackAdversarial Robustness	—Unverified
Wavelets Beat Monkeys at Adversarial Robustness	Apr 19, 2023	Adversarial AttackAdversarial Robustness	—Unverified
Fundamental Limits in Formal Verification of Message-Passing Neural Networks	Jun 10, 2022	Adversarial RobustnessClassification	—Unverified
What are effective labels for augmented data? Improving robustness with AutoLabel	Jan 1, 2021	Adversarial RobustnessData Augmentation	—Unverified
When is dataset cartography ineffective? Using training dynamics does not improve robustness against Adversarial SQuAD	Mar 24, 2025	Adversarial RobustnessExtractive Question-Answering	—Unverified
Who's Afraid of Thomas Bayes?	Jul 30, 2021	Adversarial Robustness	—Unverified
With Great Backbones Comes Great Adversarial Transferability	Jan 21, 2025	Adversarial RobustnessSelf-Supervised Learning	—Unverified
XploreNAS: Explore Adversarially Robust & Hardware-efficient Neural Architectures for Non-ideal Xbars	Feb 15, 2023	Adversarial RobustnessNeural Architecture Search	—Unverified
Your Classifier Can Do More: Towards Bridging the Gaps in Classification, Robustness, and Generation	May 26, 2025	Adversarial Robustness	—Unverified
Extreme Miscalibration and the Illusion of Adversarial Robustness	Feb 27, 2024	Adversarial AttackAdversarial Robustness	—Unverified
F^2AT: Feature-Focusing Adversarial Training via Disentanglement of Natural and Perturbed Patterns	Oct 23, 2023	Adversarial RobustnessDisentanglement	—Unverified
Facial Attributes: Accuracy and Adversarial Robustness	Jan 4, 2018	Adversarial RobustnessAttribute	—Unverified
FADE: Enabling Federated Adversarial Training on Heterogeneous Resource-Constrained Edge Devices	Sep 8, 2022	Adversarial RobustnessFederated Learning	—Unverified
FADER: Fast Adversarial Example Rejection	Oct 18, 2020	Adversarial Robustness	—Unverified
Failure Modes of Variational Autoencoders and Their Effects on Downstream Tasks	Jul 14, 2020	Adversarial Robustness	—Unverified
Fair Robust Active Learning by Joint Inconsistency	Sep 22, 2022	Active LearningAdversarial Attack	—Unverified
FAIR-TAT: Improving Model Fairness Using Targeted Adversarial Training	Oct 30, 2024	Adversarial RobustnessFairness	—Unverified
Faithful Knowledge Distillation	Jun 7, 2023	Adversarial RobustnessKnowledge Distillation	—Unverified
Fast Adversarial Training against Textual Adversarial Attacks	Jan 23, 2024	Adversarial DefenseAdversarial Robustness	—Unverified
Fast Adversarial Training with Weak-to-Strong Spatial-Temporal Consistency in the Frequency Domain on Videos	Apr 21, 2025	Adversarial RobustnessVideo Recognition	—Unverified
FAT: Federated Adversarial Training	Dec 3, 2020	Adversarial RobustnessFederated Learning	—Unverified
Fault Tolerance of Neural Networks in Adversarial Settings	Oct 30, 2019	Adversarial RobustnessFairness	—Unverified
Feature Averaging: An Implicit Bias of Gradient Descent Leading to Non-Robustness in Neural Networks	Oct 14, 2024	Adversarial RobustnessBinary Classification	—Unverified
Feature Binding with Category-Dependant MixUp for Semantic Segmentation and Adversarial Robustness	Aug 13, 2020	Adversarial RobustnessDenoising	—Unverified
Feature Distillation With Guided Adversarial Contrastive Learning	Sep 21, 2020	Adversarial RobustnessContrastive Learning	—Unverified

Show:10 25 50

← PrevPage 47 of 70Next →

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