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 1426–1450 of 1746 papers

Title	Date	Tasks	Status
Interpretable Graph Capsule Networks for Object Recognition	Dec 3, 2020	Adversarial RobustnessObject	—Unverified
FAT: Federated Adversarial Training	Dec 3, 2020	Adversarial RobustnessFederated Learning	—Unverified
How Robust are Randomized Smoothing based Defenses to Data Poisoning?	Dec 2, 2020	Adversarial RobustnessBilevel Optimization	—Unverified
Fast Adversarial Robustness Certification of Nearest Prototype Classifiers for Arbitrary Seminorms	Dec 1, 2020	Adversarial RobustnessQuantization	CodeCode Available
Adversarial Robustness Across Representation Spaces	Dec 1, 2020	Adversarial Robustnessimage-classification	—Unverified
Robust and Private Learning of Halfspaces	Nov 30, 2020	Adversarial Robustness	—Unverified
Architectural Adversarial Robustness: The Case for Deep Pursuit	Nov 29, 2020	Adversarial Robustnessglobal-optimization	—Unverified
Incorporating Hidden Layer representation into Adversarial Attacks and Defences	Nov 28, 2020	Adversarial Robustness	—Unverified
A Study on the Uncertainty of Convolutional Layers in Deep Neural Networks	Nov 27, 2020	Adversarial Robustness	—Unverified
Exposing the Robustness and Vulnerability of Hybrid 8T-6T SRAM Memory Architectures to Adversarial Attacks in Deep Neural Networks	Nov 26, 2020	Adversarial Robustness	—Unverified
aw_nas: A Modularized and Extensible NAS framework	Nov 25, 2020	Adversarial RobustnessNeural Architecture Search	—Unverified
A More Biologically Plausible Local Learning Rule for ANNs	Nov 24, 2020	Adversarial RobustnessBinary Classification	—Unverified
On Adversarial Robustness of 3D Point Cloud Classification under Adaptive Attacks	Nov 24, 2020	3D Point Cloud ClassificationAdversarial Robustness	—Unverified
Adversarial Examples for k-Nearest Neighbor Classifiers Based on Higher-Order Voronoi Diagrams	Nov 19, 2020	Adversarial Robustness	CodeCode Available
An Experimental Study of Semantic Continuity for Deep Learning Models	Nov 19, 2020	Adversarial RobustnessDeep Learning	—Unverified
Effective, Efficient and Robust Neural Architecture Search	Nov 19, 2020	Adversarial RobustnessNeural Architecture Search	—Unverified
Contextual Fusion For Adversarial Robustness	Nov 18, 2020	Adversarial Robustness	—Unverified
Robustified Domain Adaptation	Nov 18, 2020	Adversarial RobustnessDomain Adaptation	—Unverified
Probing Predictions on OOD Images via Nearest Categories	Nov 17, 2020	Adversarial RobustnessFew-Shot Learning	CodeCode Available
Towards Understanding the Regularization of Adversarial Robustness on Neural Networks	Nov 15, 2020	Adversarial Robustness	—Unverified
Bridging the Performance Gap between FGSM and PGD Adversarial Training	Nov 7, 2020	Adversarial AttackAdversarial Robustness	CodeCode Available
Recent Advances in Understanding Adversarial Robustness of Deep Neural Networks	Nov 3, 2020	Adversarial Robustness	—Unverified
Beyond cross-entropy: learning highly separable feature distributions for robust and accurate classification	Oct 29, 2020	Adversarial RobustnessGeneral Classification	—Unverified
Robustness May Be at Odds with Fairness: An Empirical Study on Class-wise Accuracy	Oct 26, 2020	Adversarial RobustnessAutonomous Driving	—Unverified
Towards Robust Neural Networks via Orthogonal Diversity	Oct 23, 2020	Adversarial RobustnessData Augmentation	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