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 1301–1325 of 1746 papers

Title	Date	Tasks	Status
Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack	Jun 15, 2022	Adversarial RobustnessComputational Efficiency	CodeCode Available
Scaling Trends in Language Model Robustness	Jul 25, 2024	Adversarial RobustnessLanguage Modeling	CodeCode Available
Exploring Adversarial Robustness of Deep Metric Learning	Feb 14, 2021	Adversarial RobustnessMetric Learning	CodeCode Available
Rethinking Robust Contrastive Learning from the Adversarial Perspective	Feb 5, 2023	Adversarial RobustnessContrastive Learning	CodeCode Available
Towards Alternative Techniques for Improving Adversarial Robustness: Analysis of Adversarial Training at a Spectrum of Perturbations	Jun 13, 2022	Adversarial RobustnessQuantization	CodeCode Available
Scaleable input gradient regularization for adversarial robustness	May 27, 2019	Adversarial AttackAdversarial Defense	CodeCode Available
Analysis and Extensions of Adversarial Training for Video Classification	Jun 16, 2022	Action RecognitionAdversarial Defense	CodeCode Available
Feature Denoising for Improving Adversarial Robustness	Dec 9, 2018	Adversarial DefenseAdversarial Robustness	CodeCode Available
Scaling Compute Is Not All You Need for Adversarial Robustness	Dec 20, 2023	Adversarial RobustnessAll	CodeCode Available
CAMP in the Odyssey: Provably Robust Reinforcement Learning with Certified Radius Maximization	Jan 29, 2025	Adversarial RobustnessDeep Reinforcement Learning	CodeCode Available
ScAR: Scaling Adversarial Robustness for LiDAR Object Detection	Dec 5, 2023	3D Object DetectionAdversarial Attack	CodeCode Available
Exploring Adversarially Robust Training for Unsupervised Domain Adaptation	Feb 18, 2022	Adversarial DefenseAdversarial Robustness	CodeCode Available
Feature Statistics with Uncertainty Help Adversarial Robustness	Mar 26, 2025	Adversarial Robustness	CodeCode Available
Exploring Adversarial Examples and Adversarial Robustness of Convolutional Neural Networks by Mutual Information	Jul 12, 2022	Adversarial Robustness	CodeCode Available
Exploring Adversarial Attacks and Defenses in Vision Transformers trained with DINO	Jun 14, 2022	Adversarial Robustness	CodeCode Available
An Adversarial Robustness Perspective on the Topology of Neural Networks	Nov 4, 2022	Adversarial Robustness	CodeCode Available
A Closer Look at Memorization in Deep Networks	Jun 16, 2017	Adversarial RobustnessMemorization	CodeCode Available
Certified Adversarial Robustness with Additive Noise	Sep 10, 2018	Adversarial AttackAdversarial Robustness	CodeCode Available
Adversarial robustness of amortized Bayesian inference	May 24, 2023	Adversarial RobustnessBayesian Inference	CodeCode Available
Explaining Adversarial Vulnerability with a Data Sparsity Hypothesis	Mar 1, 2021	Adversarial Robustness	CodeCode Available
Adversarial Robustness vs Model Compression, or Both?	Mar 29, 2019	Adversarial Robustnessmodel	CodeCode Available
CalFAT: Calibrated Federated Adversarial Training with Label Skewness	May 30, 2022	Adversarial RobustnessFederated Learning	CodeCode Available
Finding Biological Plausibility for Adversarially Robust Features via Metameric Tasks	Feb 2, 2022	Adversarial RobustnessTexture Synthesis	CodeCode Available
Understanding Adversarial Robustness Against On-manifold Adversarial Examples	Oct 2, 2022	Adversarial Robustness	CodeCode Available
Adversarial Robustness Analysis of Vision-Language Models in Medical Image Segmentation	May 5, 2025	Adversarial AttackAdversarial Robustness	CodeCode Available

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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