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	Hype
Removing Batch Normalization Boosts Adversarial Training	Jul 4, 2022	Adversarial Robustness	CodeCode Available	1
IBP Regularization for Verified Adversarial Robustness via Branch-and-Bound	Jun 29, 2022	Adversarial Robustness	CodeCode Available	0
Adversarial Ensemble Training by Jointly Learning Label Dependencies and Member Models	Jun 29, 2022	Adversarial RobustnessDiversity	CodeCode Available	0
Increasing Confidence in Adversarial Robustness Evaluations	Jun 28, 2022	Adversarial Robustness	—Unverified	0
Adversarial Robustness of Deep Neural Networks: A Survey from a Formal Verification Perspective	Jun 24, 2022	Adversarial RobustnessBIG-bench Machine Learning	—Unverified	0
Robustness of Explanation Methods for NLP Models	Jun 24, 2022	Adversarial AttackAdversarial Robustness	—Unverified	0
Robust Deep Reinforcement Learning through Bootstrapped Opportunistic Curriculum	Jun 21, 2022	Adversarial RobustnessDeep Reinforcement Learning	CodeCode Available	1
(Certified!!) Adversarial Robustness for Free!	Jun 21, 2022	Adversarial RobustnessDenoising	CodeCode Available	1
Towards Adversarial Attack on Vision-Language Pre-training Models	Jun 19, 2022	Adversarial AttackAdversarial Robustness	CodeCode Available	1
On the Limitations of Stochastic Pre-processing Defenses	Jun 19, 2022	Adversarial Robustness	CodeCode Available	0
Demystifying the Adversarial Robustness of Random Transformation Defenses	Jun 18, 2022	Adversarial RobustnessAutonomous Vehicles	CodeCode Available	0
Adversarial Robustness is at Odds with Lazy Training	Jun 18, 2022	Adversarial RobustnessLearning Theory	—Unverified	0
Existence and Minimax Theorems for Adversarial Surrogate Risks in Binary Classification	Jun 18, 2022	Adversarial RobustnessBinary Classification	—Unverified	0
Understanding Robust Overfitting of Adversarial Training and Beyond	Jun 17, 2022	Adversarial RobustnessData Ablation	CodeCode Available	1
Analysis and Extensions of Adversarial Training for Video Classification	Jun 16, 2022	Action RecognitionAdversarial Defense	CodeCode Available	0
Human Eyes Inspired Recurrent Neural Networks are More Robust Against Adversarial Noises	Jun 15, 2022	Adversarial RobustnessFoveation	CodeCode Available	0
Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack	Jun 15, 2022	Adversarial RobustnessComputational Efficiency	CodeCode Available	0
Hardening DNNs against Transfer Attacks during Network Compression using Greedy Adversarial Pruning	Jun 15, 2022	Adversarial RobustnessQuantization	CodeCode Available	0
Queried Unlabeled Data Improves and Robustifies Class-Incremental Learning	Jun 15, 2022	Adversarial Robustnessclass-incremental learning	CodeCode Available	0
Efficiently Training Low-Curvature Neural Networks	Jun 14, 2022	Adversarial Robustness	CodeCode Available	0
Exploring Adversarial Attacks and Defenses in Vision Transformers trained with DINO	Jun 14, 2022	Adversarial Robustness	CodeCode Available	0
Adversarial Vulnerability of Randomized Ensembles	Jun 14, 2022	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Towards Alternative Techniques for Improving Adversarial Robustness: Analysis of Adversarial Training at a Spectrum of Perturbations	Jun 13, 2022	Adversarial RobustnessQuantization	CodeCode Available	0
Defending Adversarial Examples by Negative Correlation Ensemble	Jun 11, 2022	Adversarial Robustness	CodeCode Available	0
Improving the Adversarial Robustness of NLP Models by Information Bottleneck	Jun 11, 2022	Adversarial RobustnessSST-2	CodeCode Available	0

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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