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 1526–1550 of 1746 papers

Title	Date	Tasks	Status	Hype
Inherent Adversarial Robustness of Deep Spiking Neural Networks: Effects of Discrete Input Encoding and Non-Linear Activations	Mar 23, 2020	Adversarial RobustnessAttribute	CodeCode Available	1
Architectural Resilience to Foreground-and-Background Adversarial Noise	Mar 23, 2020	Adversarial Robustness	CodeCode Available	0
Adversarial Robustness on In- and Out-Distribution Improves Explainability	Mar 20, 2020	Adversarial Robustnessimage-classification	CodeCode Available	1
SAT: Improving Adversarial Training via Curriculum-Based Loss Smoothing	Mar 18, 2020	Adversarial Robustness	—Unverified	0
Toward Adversarial Robustness via Semi-supervised Robust Training	Mar 16, 2020	Adversarial DefenseAdversarial Robustness	CodeCode Available	1
ARAE: Adversarially Robust Training of Autoencoders Improves Novelty Detection	Mar 12, 2020	Adversarial RobustnessNovelty Detection	CodeCode Available	1
Adversarial Machine Learning: Bayesian Perspectives	Mar 7, 2020	Adversarial RobustnessBIG-bench Machine Learning	CodeCode Available	1
Adversarial Vertex Mixup: Toward Better Adversarially Robust Generalization	Mar 5, 2020	Adversarial RobustnessData Augmentation	CodeCode Available	1
Metrics and methods for robustness evaluation of neural networks with generative models	Mar 4, 2020	Adversarial Robustnessimage-classification	CodeCode Available	0
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks	Mar 3, 2020	Adversarial Robustness	CodeCode Available	1
Learn2Perturb: an End-to-end Feature Perturbation Learning to Improve Adversarial Robustness	Mar 2, 2020	Adversarial DefenseAdversarial Robustness	CodeCode Available	1
Understanding the Intrinsic Robustness of Image Distributions using Conditional Generative Models	Mar 1, 2020	Adversarial Robustness	CodeCode Available	0
Defense-PointNet: Protecting PointNet Against Adversarial Attacks	Feb 27, 2020	Adversarial Robustness	—Unverified	0
Attacks Which Do Not Kill Training Make Adversarial Learning Stronger	Feb 26, 2020	Adversarial Robustness	CodeCode Available	1
Learning Adversarially Robust Representations via Worst-Case Mutual Information Maximization	Feb 26, 2020	Adversarial Robustness	CodeCode Available	1
Can we have it all? On the Trade-off between Spatial and Adversarial Robustness of Neural Networks	Feb 26, 2020	Adversarial RobustnessAll	CodeCode Available	0
Towards Certifiable Adversarial Sample Detection	Feb 20, 2020	Adversarial Robustness	—Unverified	0
Individual Fairness Revisited: Transferring Techniques from Adversarial Robustness	Feb 18, 2020	Adversarial RobustnessFairness	—Unverified	0
Scalable Quantitative Verification For Deep Neural Networks	Feb 17, 2020	Adversarial Robustness	—Unverified	0
Hold me tight! Influence of discriminative features on deep network boundaries	Feb 15, 2020	Adversarial Robustness	CodeCode Available	1
CEB Improves Model Robustness	Feb 13, 2020	Adversarial RobustnessData Augmentation	CodeCode Available	0
Adversarial Robustness for Code	Feb 11, 2020	Adversarial RobustnessBIG-bench Machine Learning	CodeCode Available	1
Semialgebraic Optimization for Lipschitz Constants of ReLU Networks	Feb 10, 2020	Adversarial Robustness	CodeCode Available	0
Random Smoothing Might be Unable to Certify _ Robustness for High-Dimensional Images	Feb 10, 2020	Adversarial Robustness	CodeCode Available	1
Renofeation: A Simple Transfer Learning Method for Improved Adversarial Robustness	Feb 7, 2020	Adversarial AttackAdversarial 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