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 501–525 of 1746 papers

Title	Date	Tasks	Status	Score
Data Quality Matters For Adversarial Training: An Empirical Study	Feb 15, 2021	Adversarial Robustness	CodeCode Available	5
DAT: Improving Adversarial Robustness via Generative Amplitude Mix-up in Frequency Domain	Oct 16, 2024	Adversarial Robustness	CodeCode Available	5
Batch Normalization Increases Adversarial Vulnerability and Decreases Adversarial Transferability: A Non-Robust Feature Perspective	Oct 7, 2020	Adversarial Robustness	CodeCode Available	5
Adversarial Robustness of Prompt-based Few-Shot Learning for Natural Language Understanding	Jun 19, 2023	Adversarial RobustnessFew-Shot Learning	CodeCode Available	5
Generating Adversarial Examples with Adversarial Networks	Jan 8, 2018	Adversarial Robustness	CodeCode Available	5
Deceptive Fairness Attacks on Graphs via Meta Learning	Oct 24, 2023	Adversarial RobustnessFairness	CodeCode Available	5
Generating Adversarial Samples in Mini-Batches May Be Detrimental To Adversarial Robustness	Mar 30, 2023	Adversarial Robustness	CodeCode Available	5
Global-Local Regularization Via Distributional Robustness	Mar 1, 2022	Adversarial RobustnessDomain Adaptation	CodeCode Available	5
An Adversarial Robustness Perspective on the Topology of Neural Networks	Nov 4, 2022	Adversarial Robustness	CodeCode Available	5
Improved Robustness Against Adaptive Attacks With Ensembles and Error-Correcting Output Codes	Mar 4, 2023	Adversarial RobustnessDiversity	CodeCode Available	5
Adversarial Fine-tuning of Compressed Neural Networks for Joint Improvement of Robustness and Efficiency	Mar 14, 2024	Adversarial RobustnessModel Compression	CodeCode Available	5
Gated Information Bottleneck for Generalization in Sequential Environments	Oct 12, 2021	Adversarial RobustnessOut of Distribution (OOD) Detection	CodeCode Available	5
AutoZOOM: Autoencoder-based Zeroth Order Optimization Method for Attacking Black-box Neural Networks	May 30, 2018	Adversarial Robustness	CodeCode Available	5
Automated Repair of Neural Networks	Jul 17, 2022	Adversarial Robustness	CodeCode Available	5
Adversarial Robustness of Neural-Statistical Features in Detection of Generative Transformers	Mar 2, 2022	Adversarial RobustnessAdversarial Text	CodeCode Available	5
Adversarial Robustness of MR Image Reconstruction under Realistic Perturbations	Aug 5, 2022	Adversarial RobustnessDiagnostic	CodeCode Available	5
Adaptive Smoothness-weighted Adversarial Training for Multiple Perturbations with Its Stability Analysis	Oct 2, 2022	Adversarial Robustness	CodeCode Available	5
GAT: Guided Adversarial Training with Pareto-optimal Auxiliary Tasks	Feb 6, 2023	Adversarial RobustnessData Augmentation	CodeCode Available	5
Finding Biological Plausibility for Adversarially Robust Features via Metameric Tasks	Feb 2, 2022	Adversarial RobustnessTexture Synthesis	CodeCode Available	5
FI-ODE: Certifiably Robust Forward Invariance in Neural ODEs	Oct 30, 2022	Adversarial Robustnesscontinuous-control	CodeCode Available	5
Adversarial Feature Desensitization	Jun 8, 2020	Adversarial RobustnessDomain Adaptation	CodeCode Available	5
Improving Adversarial Robustness by Putting More Regularizations on Less Robust Samples	Jun 7, 2022	Adversarial Robustness	CodeCode Available	5
Feature Statistics with Uncertainty Help Adversarial Robustness	Mar 26, 2025	Adversarial Robustness	CodeCode Available	5
Efficiently Training Low-Curvature Neural Networks	Jun 14, 2022	Adversarial Robustness	CodeCode Available	5
GenAttack: Practical Black-box Attacks with Gradient-Free Optimization	May 28, 2018	Adversarial AttackAdversarial Robustness	CodeCode Available	5

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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