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 1201–1225 of 1746 papers

Title	Date	Tasks	Status	Hype
Adversarial Robustness via Fisher-Rao Regularization	Jun 12, 2021	Adversarial DefenseAdversarial Robustness	CodeCode Available	0
CARTL: Cooperative Adversarially-Robust Transfer Learning	Jun 12, 2021	Adversarial RobustnessTransfer Learning	CodeCode Available	0
Corruption-Robust Offline Reinforcement Learning	Jun 11, 2021	Adversarial RobustnessOffline RL	—Unverified	0
CausalAdv: Adversarial Robustness through the Lens of Causality	Jun 11, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Improving White-box Robustness of Pre-processing Defenses via Joint Adversarial Training	Jun 10, 2021	Adversarial DefenseAdversarial Robustness	—Unverified	0
An Ensemble Approach Towards Adversarial Robustness	Jun 10, 2021	Adversarial RobustnessRobust classification	—Unverified	0
Towards Defending against Adversarial Examples via Attack-Invariant Features	Jun 9, 2021	Adversarial Robustness	—Unverified	0
Reliable Adversarial Distillation with Unreliable Teachers	Jun 9, 2021	Adversarial Robustness	CodeCode Available	1
Towards the Memorization Effect of Neural Networks in Adversarial Training	Jun 9, 2021	Adversarial RobustnessMemorization	—Unverified	0
Adversarial Attack and Defense in Deep Ranking	Jun 7, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1
RoSearch: Search for Robust Student Architectures When Distilling Pre-trained Language Models	Jun 7, 2021	Adversarial RobustnessKnowledge Distillation	—Unverified	0
A Primer on Multi-Neuron Relaxation-based Adversarial Robustness Certification	Jun 6, 2021	Adversarial Robustness	—Unverified	0
k-Mixup Regularization for Deep Learning via Optimal Transport	Jun 5, 2021	Adversarial RobustnessDeep Learning	CodeCode Available	0
Revisiting Hilbert-Schmidt Information Bottleneck for Adversarial Robustness	Jun 4, 2021	Adversarial Robustness	CodeCode Available	1
Improving Neural Network Robustness via Persistency of Excitation	Jun 3, 2021	Adversarial Robustnessparameter estimation	CodeCode Available	0
PDPGD: Primal-Dual Proximal Gradient Descent Adversarial Attack	Jun 3, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	0
Certified Robustness to Word Substitution Attack with Differential Privacy	Jun 1, 2021	Adversarial RobustnessClassification	—Unverified	0
Improving the Adversarial Robustness for Speaker Verification by Self-Supervised Learning	Jun 1, 2021	Adversarial DefenseAdversarial Robustness	—Unverified	0
Variational Autoencoders: A Harmonic Perspective	May 31, 2021	Adversarial RobustnessDecoder	—Unverified	0
NoiLIn: Improving Adversarial Training and Correcting Stereotype of Noisy Labels	May 31, 2021	Adversarial Robustness	CodeCode Available	0
Demotivate adversarial defense in remote sensing	May 28, 2021	Adversarial DefenseAdversarial Robustness	—Unverified	0
Robust Regularization with Adversarial Labelling of Perturbed Samples	May 28, 2021	Adversarial RobustnessComputational Efficiency	—Unverified	0
On Linear Stability of SGD and Input-Smoothness of Neural Networks	May 27, 2021	Adversarial Robustness	CodeCode Available	0
Adversarial Robustness against Multiple and Single l_p-Threat Models via Quick Fine-Tuning of Robust Classifiers	May 26, 2021	Adversarial Robustness	CodeCode Available	1
Deep Repulsive Prototypes for Adversarial Robustness	May 26, 2021	Adversarial Robustness	—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