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 1476–1500 of 1746 papers

Title	Date	Tasks	Status
Deep Repulsive Prototypes for Adversarial Robustness	May 26, 2021	Adversarial Robustness	—Unverified
DeepSafe: A Data-driven Approach for Checking Adversarial Robustness in Neural Networks	Oct 2, 2017	Adversarial RobustnessClustering	—Unverified
Adversary Agnostic Robust Deep Reinforcement Learning	Aug 14, 2020	Adversarial RobustnessAtari Games	—Unverified
Scalable Neural Learning for Verifiable Consistency with Temporal Specifications	Sep 25, 2019	Adversarial RobustnessLanguage Modeling	—Unverified
Defending Against Adversarial Examples by Regularized Deep Embedding	Sep 25, 2019	Adversarial AttackAdversarial Robustness	—Unverified
Defending against Adversarial Malware Attacks on ML-based Android Malware Detection Systems	Jan 23, 2025	Adversarial RobustnessAndroid Malware Detection	—Unverified
Defending Against Multiple and Unforeseen Adversarial Videos	Sep 11, 2020	Adversarial RobustnessGeneral Classification	—Unverified
Defending From Physically-Realizable Adversarial Attacks Through Internal Over-Activation Analysis	Mar 14, 2022	Adversarial Robustnessobject-detection	—Unverified
Causal Fair Metric: Bridging Causality, Individual Fairness, and Adversarial Robustness	Oct 30, 2023	Adversarial Robustnesscounterfactual	—Unverified
Defense-PointNet: Protecting PointNet Against Adversarial Attacks	Feb 27, 2020	Adversarial Robustness	—Unverified
Defense Through Diverse Directions	Mar 24, 2020	Adversarial Robustness	—Unverified
Delving into Decision-based Black-box Attacks on Semantic Segmentation	Feb 2, 2024	Adversarial RobustnessSegmentation	—Unverified
Delving into Feature Space: Improving Adversarial Robustness by Feature Spectral Regularization	Sep 29, 2021	Adversarial RobustnessAttribute	—Unverified
Delving into the Adversarial Robustness of Federated Learning	Feb 19, 2023	Adversarial RobustnessFederated Learning	—Unverified
Causal Adversarial Perturbations for Individual Fairness and Robustness in Heterogeneous Data Spaces	Aug 17, 2023	Adversarial RobustnessFairness	—Unverified
Scalable Quantitative Verification For Deep Neural Networks	Feb 17, 2020	Adversarial Robustness	—Unverified
Demotivate adversarial defense in remote sensing	May 28, 2021	Adversarial DefenseAdversarial Robustness	—Unverified
Catastrophic Overfitting: A Potential Blessing in Disguise	Feb 28, 2024	Adversarial Robustness	—Unverified
CARE: Ensemble Adversarial Robustness Evaluation Against Adaptive Attackers for Security Applications	Jan 20, 2024	Adversarial Robustness	—Unverified
Scalable Whitebox Attacks on Tree-based Models	Mar 31, 2022	Adversarial Robustness	—Unverified
CAP-GAN: Towards Adversarial Robustness with Cycle-consistent Attentional Purification	Feb 15, 2021	Adversarial AttackAdversarial Robustness	—Unverified
Denoising Autoencoder-based Defensive Distillation as an Adversarial Robustness Algorithm	Mar 28, 2023	Adversarial RobustnessData Poisoning	—Unverified
Visual Interpretability Alone Helps Adversarial Robustness	Sep 25, 2019	Adversarial Robustness	—Unverified
DensePure: Understanding Diffusion Models towards Adversarial Robustness	Nov 1, 2022	Adversarial RobustnessDenoising	—Unverified
Adversarially Robust Spiking Neural Networks with Sparse Connectivity	May 16, 2025	Adversarial Robustness	—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