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 926–950 of 1746 papers

Title	Date	Tasks	Status
The Effects of Image Distribution and Task on Adversarial Robustness	Feb 21, 2021	Adversarial RobustnessObject Recognition	—Unverified
Improving Calibration through the Relationship with Adversarial Robustness	Jun 29, 2020	Adversarial Robustness	—Unverified
Improving White-box Robustness of Pre-processing Defenses via Joint Adversarial Training	Jun 10, 2021	Adversarial DefenseAdversarial Robustness	—Unverified
In and Out-of-Domain Text Adversarial Robustness via Label Smoothing	Dec 20, 2022	Adversarial Robustness	—Unverified
The Importance of Robust Features in Mitigating Catastrophic Forgetting	Jun 29, 2023	Adversarial RobustnessContinual Learning	—Unverified
Increasing Confidence in Adversarial Robustness Evaluations	Jun 28, 2022	Adversarial Robustness	—Unverified
The Intrinsic Dimension of Images and Its Impact on Learning	Oct 10, 2020	Adversarial Robustness	—Unverified
A Random Ensemble of Encrypted Vision Transformers for Adversarially Robust Defense	Feb 11, 2024	Adversarial Robustnessimage-classification	—Unverified
Indirect Gradient Matching for Adversarial Robust Distillation	Dec 6, 2023	Adversarial RobustnessData Augmentation	—Unverified
Understanding the Interplay between Privacy and Robustness in Federated Learning	Jun 13, 2021	Adversarial RobustnessFederated Learning	—Unverified
Individual Fairness Revisited: Transferring Techniques from Adversarial Robustness	Feb 18, 2020	Adversarial RobustnessFairness	—Unverified
The Lipschitz Constant of Self-Attention	Jun 8, 2020	Adversarial RobustnessLanguage Modelling	—Unverified
Adversarially Robust Bloom Filters: Privacy, Reductions, and Open Problems	Jan 27, 2025	Adversarial Robustnessmodel	—Unverified
The Many Faces of Adversarial Risk	Jan 22, 2022	Adversarial Robustness	—Unverified
Inter-frame Accelerate Attack against Video Interpolation Models	May 11, 2023	Adversarial RobustnessVideo Frame Interpolation	—Unverified
Theoretical Analysis of Adversarial Learning: A Minimax Approach	Nov 13, 2018	Adversarial RobustnessGeneral Classification	—Unverified
A Primer on Multi-Neuron Relaxation-based Adversarial Robustness Certification	Jun 6, 2021	Adversarial Robustness	—Unverified
Interpretable Graph Capsule Networks for Object Recognition	Dec 3, 2020	Adversarial RobustnessObject	—Unverified
Interpreting Adversarial Examples by Activation Promotion and Suppression	Apr 3, 2019	Adversarial Robustness	—Unverified
Interpreting Adversarial Robustness: A View from Decision Surface in Input Space	Sep 29, 2018	Adversarial Robustness	—Unverified
Interpreting and Improving Adversarial Robustness of Deep Neural Networks with Neuron Sensitivity	Sep 16, 2019	Adversarial RobustnessDecision Making	—Unverified
Intriguing class-wise properties of adversarial training	Jan 1, 2021	Adversarial Robustness	—Unverified
Intriguing Frequency Interpretation of Adversarial Robustness for CNNs and ViTs	Jun 15, 2025	Adversarial Robustnessimage-classification	—Unverified
Intriguing Properties of Adversarial Examples	Nov 8, 2017	Adversarial RobustnessNeural Architecture Search	—Unverified
Intriguing properties of adversarial training at scale	Jun 10, 2019	Adversarial Robustness	—Unverified

Show:10 25 50

← PrevPage 38 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