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 1076–1100 of 1746 papers

Title	Date	Tasks	Status
Robustness of Explanation Methods for NLP Models	Jun 24, 2022	Adversarial AttackAdversarial Robustness	—Unverified
Adversarial Robustness of Deep Neural Networks: A Survey from a Formal Verification Perspective	Jun 24, 2022	Adversarial RobustnessBIG-bench Machine Learning	—Unverified
On the Limitations of Stochastic Pre-processing Defenses	Jun 19, 2022	Adversarial Robustness	CodeCode Available
Demystifying the Adversarial Robustness of Random Transformation Defenses	Jun 18, 2022	Adversarial RobustnessAutonomous Vehicles	CodeCode Available
Existence and Minimax Theorems for Adversarial Surrogate Risks in Binary Classification	Jun 18, 2022	Adversarial RobustnessBinary Classification	—Unverified
Adversarial Robustness is at Odds with Lazy Training	Jun 18, 2022	Adversarial RobustnessLearning Theory	—Unverified
Analysis and Extensions of Adversarial Training for Video Classification	Jun 16, 2022	Action RecognitionAdversarial Defense	CodeCode Available
Human Eyes Inspired Recurrent Neural Networks are More Robust Against Adversarial Noises	Jun 15, 2022	Adversarial RobustnessFoveation	CodeCode Available
Queried Unlabeled Data Improves and Robustifies Class-Incremental Learning	Jun 15, 2022	Adversarial Robustnessclass-incremental learning	CodeCode Available
Hardening DNNs against Transfer Attacks during Network Compression using Greedy Adversarial Pruning	Jun 15, 2022	Adversarial RobustnessQuantization	CodeCode Available
Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack	Jun 15, 2022	Adversarial RobustnessComputational Efficiency	CodeCode Available
Exploring Adversarial Attacks and Defenses in Vision Transformers trained with DINO	Jun 14, 2022	Adversarial Robustness	CodeCode Available
Efficiently Training Low-Curvature Neural Networks	Jun 14, 2022	Adversarial Robustness	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
Improving the Adversarial Robustness of NLP Models by Information Bottleneck	Jun 11, 2022	Adversarial RobustnessSST-2	CodeCode Available
Defending Adversarial Examples by Negative Correlation Ensemble	Jun 11, 2022	Adversarial Robustness	CodeCode Available
Fundamental Limits in Formal Verification of Message-Passing Neural Networks	Jun 10, 2022	Adversarial RobustnessClassification	—Unverified
Wavelet Regularization Benefits Adversarial Training	Jun 8, 2022	Adversarial Robustness	CodeCode Available
LADDER: Latent Boundary-guided Adversarial Training	Jun 8, 2022	Adversarial Robustness	CodeCode Available
Improving Adversarial Robustness by Putting More Regularizations on Less Robust Samples	Jun 7, 2022	Adversarial Robustness	CodeCode Available
Building Robust Ensembles via Margin Boosting	Jun 7, 2022	Adversarial Robustness	CodeCode Available
A Robust Backpropagation-Free Framework for Images	Jun 3, 2022	Adversarial Robustness	CodeCode Available
Adaptive Adversarial Training to Improve Adversarial Robustness of DNNs for Medical Image Segmentation and Detection	Jun 2, 2022	Adversarial RobustnessImage Segmentation	—Unverified
The robust way to stack and bag: the local Lipschitz way	Jun 1, 2022	Adversarial Robustness	—Unverified
Sequential Bayesian Neural Subnetwork Ensembles	Jun 1, 2022	Adversarial RobustnessOut-of-Distribution Detection	—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