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 1226–1250 of 1746 papers

Title	Date	Tasks	Status	Hype
Practical Convex Formulation of Robust One-hidden-layer Neural Network Training	May 25, 2021	Adversarial RobustnessBinary Classification	—Unverified	0
Skew Orthogonal Convolutions	May 24, 2021	Adversarial Robustness	CodeCode Available	1
Adversarial Examples for k-Nearest Neighbor Classifiers Based on Higher-Order Voronoi Diagrams	May 21, 2021	Adversarial Robustness	—Unverified	0
An Orthogonal Classifier for Improving the Adversarial Robustness of Neural Networks	May 19, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Adversarial examples attack based on random warm restart mechanism and improved Nesterov momentum	May 10, 2021	Adversarial RobustnessDeep Learning	—Unverified	0
Efficiency-driven Hardware Optimization for Adversarially Robust Neural Networks	May 9, 2021	Adversarial Robustness	—Unverified	0
Dynamic Defense Approach for Adversarial Robustness in Deep Neural Networks via Stochastic Ensemble Smoothed Model	May 6, 2021	Adversarial RobustnessAttribute	—Unverified	0
A Finer Calibration Analysis for Adversarial Robustness	May 4, 2021	Adversarial RobustnessBIG-bench Machine Learning	—Unverified	0
On the Adversarial Robustness of Quantized Neural Networks	May 1, 2021	Adversarial RobustnessModel Compression	—Unverified	0
Impact of Spatial Frequency Based Constraints on Adversarial Robustness	Apr 26, 2021	Adversarial Robustness	—Unverified	0
Random Noise Defense Against Query-Based Black-Box Attacks	Apr 23, 2021	Adversarial Robustness	CodeCode Available	1
Towards Adversarial Patch Analysis and Certified Defense against Crowd Counting	Apr 22, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	0
Robust Certification for Laplace Learning on Geometric Graphs	Apr 22, 2021	Adversarial AttackAdversarial Robustness	—Unverified	0
Mixture of Robust Experts (MoRE):A Robust Denoising Method towards multiple perturbations	Apr 21, 2021	Adversarial RobustnessDenoising	—Unverified	0
Calibration and Consistency of Adversarial Surrogate Losses	Apr 19, 2021	Adversarial Robustness	—Unverified	0
Removing Adversarial Noise in Class Activation Feature Space	Apr 19, 2021	Adversarial RobustnessDenoising	—Unverified	0
Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness?	Apr 19, 2021	Adversarial RobustnessImage Classification	CodeCode Available	1
On the Sensitivity and Stability of Model Interpretations in NLP	Apr 18, 2021	Adversarial RobustnessDependency Parsing	CodeCode Available	0
Does language help generalization in vision models?	Apr 16, 2021	Adversarial RobustnessClustering	CodeCode Available	0
Orthogonalizing Convolutional Layers with the Cayley Transform	Apr 14, 2021	Adversarial Robustness	CodeCode Available	1
Improved Branch and Bound for Neural Network Verification via Lagrangian Decomposition	Apr 14, 2021	Adversarial RobustnessGPU	—Unverified	0
Relating Adversarially Robust Generalization to Flat Minima	Apr 9, 2021	Adversarial Robustness	—Unverified	0
Adversarial Robustness Guarantees for Gaussian Processes	Apr 7, 2021	Adversarial RobustnessGaussian Processes	CodeCode Available	0
Universal Adversarial Training with Class-Wise Perturbations	Apr 7, 2021	Adversarial Robustness	—Unverified	0
Adversarial Robustness under Long-Tailed Distribution	Apr 6, 2021	Adversarial Robustness	CodeCode Available	1

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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