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 1001–1025 of 1746 papers

Title	Date	Tasks	Status
Topological Signatures of Adversaries in Multimodal Alignments	Jan 29, 2025	Adversarial Robustness	—Unverified
Likelihood Landscapes: A Unifying Principle Behind Many Adversarial Defenses	Aug 25, 2020	Adversarial DefenseAdversarial Robustness	—Unverified
Generalized No Free Lunch Theorem for Adversarial Robustness	Oct 8, 2018	Adversarial Robustness	—Unverified
Limited but consistent gains in adversarial robustness by co-training object recognition models with human EEG	Sep 5, 2024	Adversarial RobustnessEEG	—Unverified
Lipschitz Constant Meets Condition Number: Learning Robust and Compact Deep Neural Networks	Mar 26, 2025	Adversarial RobustnessNetwork Pruning	—Unverified
Lipschitz regularized Deep Neural Networks generalize and are adversarially robust	Aug 28, 2018	Adversarial Robustness	—Unverified
Local Competition and Stochasticity for Adversarial Robustness in Deep Learning	Jan 4, 2021	Adversarial AttackAdversarial Robustness	—Unverified
Local Competition and Uncertainty for Adversarial Robustness in Deep Learning	Jun 18, 2020	Adversarial AttackAdversarial Robustness	—Unverified
Logit Calibration and Feature Contrast for Robust Federated Learning on Non-IID Data	Apr 10, 2024	Adversarial RobustnessFederated Learning	—Unverified
A3E: Aligned and Augmented Adversarial Ensemble for Accurate, Robust and Privacy-Preserving EEG Decoding	Dec 16, 2024	Adversarial RobustnessBrain Computer Interface	—Unverified
Long-tailed Adversarial Training with Self-Distillation	Mar 9, 2025	Adversarial Robustness	—Unverified
Accelerating Adversarial Perturbation by 50% with Semi-backward Propagation	Nov 9, 2022	Adversarial Robustness	—Unverified
An empirical study of pretrained representations for few-shot classification	Oct 3, 2019	Adversarial RobustnessClassification	—Unverified
Toward Adversarial Robustness by Diversity in an Ensemble of Specialized Deep Neural Networks	May 17, 2020	Adversarial RobustnessDiversity	—Unverified
LOTS about Attacking Deep Features	Nov 18, 2016	Adversarial Robustness	—Unverified
Adversarial Attacks and Defenses for Speech Recognition Systems	Mar 31, 2021	Adversarial RobustnessAutomatic Speech Recognition	—Unverified
Lower Difficulty and Better Robustness: A Bregman Divergence Perspective for Adversarial Training	Aug 26, 2022	Adversarial Robustness	—Unverified
L_p-norm Distortion-Efficient Adversarial Attack	Jul 3, 2024	Adversarial AttackAdversarial Defense	—Unverified
Towards Reliable Neural Specifications	Oct 28, 2022	Adversarial Robustness	—Unverified
An Empirical Study of Accuracy, Fairness, Explainability, Distributional Robustness, and Adversarial Robustness	Sep 29, 2021	Adversarial RobustnessFairness	—Unverified
Lyapunov Neural ODE State-Feedback Control Policies	Aug 31, 2024	Adversarial RobustnessDecision Making	—Unverified
Maintaining Adversarial Robustness in Continuous Learning	Feb 17, 2024	Adversarial RobustnessContinual Learning	—Unverified
Manifold-aware Training: Increase Adversarial Robustness with Feature Clustering	Jan 1, 2021	Adversarial RobustnessClustering	—Unverified
Adapters Mixup: Mixing Parameter-Efficient Adapters to Enhance the Adversarial Robustness of Fine-tuned Pre-trained Text Classifiers	Jan 18, 2024	Adversarial Robustnessparameter-efficient fine-tuning	—Unverified
And/or trade-off in artificial neurons: impact on adversarial robustness	Feb 15, 2021	Adversarial RobustnessBIG-bench Machine Learning	—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