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 901–925 of 1746 papers

Title	Date	Tasks	Status
Are models trained on temporally-continuous data streams more adversarially robust?	Oct 12, 2021	Adversarial Robustness	—Unverified
Understanding the Impact of Graph Reduction on Adversarial Robustness in Graph Neural Networks	Dec 8, 2024	Adversarial Robustness	—Unverified
Improving deep learning with prior knowledge and cognitive models: A survey on enhancing explainability, adversarial robustness and zero-shot learning	Mar 11, 2024	Adversarial RobustnessDeep Learning	—Unverified
The Dilemma Between Data Transformations and Adversarial Robustness for Time Series Application Systems	Jun 18, 2020	Adversarial RobustnessDecision Making	—Unverified
The Diversity Metrics of Sub-models based on SVD of Jacobians for Ensembles Adversarial Robustness	Nov 22, 2021	Adversarial RobustnessAttribute	—Unverified
Improving Fast Adversarial Training via Self-Knowledge Guidance	Sep 26, 2024	Adversarial RobustnessAttribute	—Unverified
Are classical deep neural networks weakly adversarially robust?	May 28, 2025	Adversarial DefenseAdversarial Robustness	—Unverified
Improving Generalization of Adversarial Training via Robust Critical Fine-Tuning	Aug 1, 2023	Adversarial Robustness	—Unverified
Improving Global Adversarial Robustness Generalization With Adversarially Trained GAN	Mar 8, 2021	Adversarial DefenseAdversarial Robustness	—Unverified
Improving Graph Neural Networks via Adversarial Robustness Evaluation	Dec 14, 2024	Adversarial Robustness	—Unverified
Improving Hierarchical Adversarial Robustness of Deep Neural Networks	Feb 17, 2021	Adversarial DefenseAdversarial Robustness	—Unverified
Improving Hyperspectral Adversarial Robustness Under Multiple Attacks	Oct 28, 2022	Adversarial RobustnessSemantic Segmentation	—Unverified
Improving Interpretability via Regularization of Neural Activation Sensitivity	Nov 16, 2022	Adversarial RobustnessExplanation Fidelity Evaluation	—Unverified
The Double-Edged Sword of Implicit Bias: Generalization vs. Robustness in ReLU Networks	Mar 2, 2023	Adversarial Robustness	—Unverified
Improving out-of-distribution generalization via multi-task self-supervised pretraining	Mar 30, 2020	Adversarial RobustnessDomain Generalization	—Unverified
Improving Performance of Semi-Supervised Learning by Adversarial Attacks	Aug 8, 2023	Adversarial Robustnessimage-classification	—Unverified
Improving Robust Generalization by Direct PAC-Bayesian Bound Minimization	Nov 22, 2022	Adversarial Robustness	—Unverified
Improving Robustness Against Adversarial Attacks with Deeply Quantized Neural Networks	Apr 25, 2023	Adversarial RobustnessQuantization	—Unverified
Improving the Adversarial Robustness for Speaker Verification by Self-Supervised Learning	Jun 1, 2021	Adversarial DefenseAdversarial Robustness	—Unverified
Improving Robustness of Deep Convolutional Neural Networks via Multiresolution Learning	Sep 24, 2023	Adversarial Robustness	—Unverified
Are Adversarial Robustness and Common Perturbation Robustness Independent Attributes ?	Sep 4, 2019	Adversarial Robustness	—Unverified
Improving Robustness with Image Filtering	Dec 21, 2021	Adversarial RobustnessData Augmentation	—Unverified
Architectural Adversarial Robustness: The Case for Deep Pursuit	Nov 29, 2020	Adversarial Robustnessglobal-optimization	—Unverified
The Effect of Prior Lipschitz Continuity on the Adversarial Robustness of Bayesian Neural Networks	Jan 7, 2021	Adversarial RobustnessVariational Inference	—Unverified
ARBiBench: Benchmarking Adversarial Robustness of Binarized Neural Networks	Dec 21, 2023	Adversarial RobustnessBenchmarking	—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