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 201–225 of 1746 papers

Title	Date	Tasks	Status	Hype	Score
Learning Adversarially Robust Representations via Worst-Case Mutual Information Maximization	Feb 26, 2020	Adversarial Robustness	CodeCode Available	1	5
AGKD-BML: Defense Against Adversarial Attack by Attention Guided Knowledge Distillation and Bi-directional Metric Learning	Aug 13, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
Consistency Regularization for Adversarial Robustness	Mar 8, 2021	Adversarial RobustnessData Augmentation	CodeCode Available	1	5
MENLI: Robust Evaluation Metrics from Natural Language Inference	Aug 15, 2022	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
Broken Neural Scaling Laws	Oct 26, 2022	Adversarial RobustnessContinual Learning	CodeCode Available	1	5
Mitigating Adversarial Vulnerability through Causal Parameter Estimation by Adversarial Double Machine Learning	Jul 14, 2023	Adversarial Robustnessparameter estimation	CodeCode Available	1	5
CARBEN: Composite Adversarial Robustness Benchmark	Jul 16, 2022	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
Certified Adversarial Robustness via Randomized Smoothing	Feb 8, 2019	Adversarial DefenseAdversarial Robustness	CodeCode Available	1	5
CARLA-GeAR: a Dataset Generator for a Systematic Evaluation of Adversarial Robustness of Vision Models	Jun 9, 2022	Adversarial DefenseAdversarial Robustness	CodeCode Available	1	5
Multitask Learning Strengthens Adversarial Robustness	Jul 14, 2020	Adversarial DefenseAdversarial Robustness	CodeCode Available	1	5
Decoupled Adversarial Contrastive Learning for Self-supervised Adversarial Robustness	Jul 22, 2022	Adversarial RobustnessContrastive Learning	CodeCode Available	1	5
The Eigenlearning Framework: A Conservation Law Perspective on Kernel Regression and Wide Neural Networks	Oct 8, 2021	Adversarial RobustnessInductive Bias	CodeCode Available	1	5
OET: Optimization-based prompt injection Evaluation Toolkit	May 1, 2025	Adversarial RobustnessNatural Language Understanding	CodeCode Available	1	5
Cauchy-Schwarz Divergence Information Bottleneck for Regression	Apr 27, 2024	Adversarial RobustnessInformation Plane	CodeCode Available	1	5
Efficient Image-to-Image Diffusion Classifier for Adversarial Robustness	Aug 16, 2024	Adversarial Robustnessimage-classification	CodeCode Available	1	5
AdvDrop: Adversarial Attack to DNNs by Dropping Information	Aug 20, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks	Nov 1, 2022	Adversarial RobustnessPseudo Label	CodeCode Available	1	5
Adversarial Vertex Mixup: Toward Better Adversarially Robust Generalization	Mar 5, 2020	Adversarial RobustnessData Augmentation	CodeCode Available	1	5
Adversarial Visual Robustness by Causal Intervention	Jun 17, 2021	Adversarial Robustness	CodeCode Available	1	5
Adversarial vulnerability of powerful near out-of-distribution detection	Jan 18, 2022	Adversarial RobustnessOut-of-Distribution Detection	CodeCode Available	1	5
Adversarial Vulnerability of Randomized Ensembles	Jun 14, 2022	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
Adversarial Machine Learning: Bayesian Perspectives	Mar 7, 2020	Adversarial RobustnessBIG-bench Machine Learning	CodeCode Available	1	5
GenoArmory: A Unified Evaluation Framework for Adversarial Attacks on Genomic Foundation Models	May 16, 2025	Adversarial AttackAdversarial Defense	CodeCode Available	1	5
Certified Training: Small Boxes are All You Need	Oct 10, 2022	Adversarial RobustnessAll	CodeCode Available	1	5
Federated Robustness Propagation: Sharing Robustness in Heterogeneous Federated Learning	Jun 18, 2021	Adversarial RobustnessFederated Learning	CodeCode Available	1	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