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 1351–1375 of 1746 papers

Title	Date	Tasks	Status	Hype
On 1/n neural representation and robustness	Dec 8, 2020	Adversarial Robustness	CodeCode Available	0
Overcomplete Representations Against Adversarial Videos	Dec 8, 2020	Adversarial RobustnessDecoder	CodeCode Available	0
Using Feature Alignment Can Improve Clean Average Precision and Adversarial Robustness in Object Detection	Dec 8, 2020	2D Object DetectionAdversarial Attack	CodeCode Available	1
Evaluating adversarial robustness in simulated cerebellum	Dec 5, 2020	Adversarial Robustness	—Unverified	0
Unsupervised Adversarially-Robust Representation Learning on Graphs	Dec 4, 2020	Adversarial RobustnessCommunity Detection	—Unverified	0
FAT: Federated Adversarial Training	Dec 3, 2020	Adversarial RobustnessFederated Learning	—Unverified	0
Interpretable Graph Capsule Networks for Object Recognition	Dec 3, 2020	Adversarial RobustnessObject	—Unverified	0
Stochastic Gradient Descent with Nonlinear Conjugate Gradient-Style Adaptive Momentum	Dec 3, 2020	Adversarial Robustness	—Unverified	0
How Robust are Randomized Smoothing based Defenses to Data Poisoning?	Dec 2, 2020	Adversarial RobustnessBilevel Optimization	—Unverified	0
Adversarial Robustness Across Representation Spaces	Dec 1, 2020	Adversarial Robustnessimage-classification	—Unverified	0
Fast Adversarial Robustness Certification of Nearest Prototype Classifiers for Arbitrary Seminorms	Dec 1, 2020	Adversarial RobustnessQuantization	CodeCode Available	0
On the Trade-off between Adversarial and Backdoor Robustness	Dec 1, 2020	Adversarial Robustness	CodeCode Available	1
Robust and Private Learning of Halfspaces	Nov 30, 2020	Adversarial Robustness	—Unverified	0
Architectural Adversarial Robustness: The Case for Deep Pursuit	Nov 29, 2020	Adversarial Robustnessglobal-optimization	—Unverified	0
Incorporating Hidden Layer representation into Adversarial Attacks and Defences	Nov 28, 2020	Adversarial Robustness	—Unverified	0
A Study on the Uncertainty of Convolutional Layers in Deep Neural Networks	Nov 27, 2020	Adversarial Robustness	—Unverified	0
Regularization with Latent Space Virtual Adversarial Training	Nov 26, 2020	Adversarial Robustnessimage-classification	CodeCode Available	1
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	0
aw_nas: A Modularized and Extensible NAS framework	Nov 25, 2020	Adversarial RobustnessNeural Architecture Search	—Unverified	0
On Adversarial Robustness of 3D Point Cloud Classification under Adaptive Attacks	Nov 24, 2020	3D Point Cloud ClassificationAdversarial Robustness	—Unverified	0
A More Biologically Plausible Local Learning Rule for ANNs	Nov 24, 2020	Adversarial RobustnessBinary Classification	—Unverified	0
A Reputation Mechanism Is All You Need: Collaborative Fairness and Adversarial Robustness in Federated Learning	Nov 20, 2020	Adversarial DefenseAdversarial Robustness	CodeCode Available	1
Adversarial Examples for k-Nearest Neighbor Classifiers Based on Higher-Order Voronoi Diagrams	Nov 19, 2020	Adversarial Robustness	CodeCode Available	0
Effective, Efficient and Robust Neural Architecture Search	Nov 19, 2020	Adversarial RobustnessNeural Architecture Search	—Unverified	0
An Experimental Study of Semantic Continuity for Deep Learning Models	Nov 19, 2020	Adversarial RobustnessDeep Learning	—Unverified	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