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 1301–1325 of 1746 papers

Title	Date	Tasks	Status
An Ensemble Approach Towards Adversarial Robustness	Jun 10, 2021	Adversarial RobustnessRobust classification	—Unverified
Improving White-box Robustness of Pre-processing Defenses via Joint Adversarial Training	Jun 10, 2021	Adversarial DefenseAdversarial Robustness	—Unverified
Towards Defending against Adversarial Examples via Attack-Invariant Features	Jun 9, 2021	Adversarial Robustness	—Unverified
Towards the Memorization Effect of Neural Networks in Adversarial Training	Jun 9, 2021	Adversarial RobustnessMemorization	—Unverified
RoSearch: Search for Robust Student Architectures When Distilling Pre-trained Language Models	Jun 7, 2021	Adversarial RobustnessKnowledge Distillation	—Unverified
A Primer on Multi-Neuron Relaxation-based Adversarial Robustness Certification	Jun 6, 2021	Adversarial Robustness	—Unverified
k-Mixup Regularization for Deep Learning via Optimal Transport	Jun 5, 2021	Adversarial RobustnessDeep Learning	CodeCode Available
Improving Neural Network Robustness via Persistency of Excitation	Jun 3, 2021	Adversarial Robustnessparameter estimation	CodeCode Available
PDPGD: Primal-Dual Proximal Gradient Descent Adversarial Attack	Jun 3, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available
Certified Robustness to Word Substitution Attack with Differential Privacy	Jun 1, 2021	Adversarial RobustnessClassification	—Unverified
Improving the Adversarial Robustness for Speaker Verification by Self-Supervised Learning	Jun 1, 2021	Adversarial DefenseAdversarial Robustness	—Unverified
NoiLIn: Improving Adversarial Training and Correcting Stereotype of Noisy Labels	May 31, 2021	Adversarial Robustness	CodeCode Available
Variational Autoencoders: A Harmonic Perspective	May 31, 2021	Adversarial RobustnessDecoder	—Unverified
Demotivate adversarial defense in remote sensing	May 28, 2021	Adversarial DefenseAdversarial Robustness	—Unverified
Robust Regularization with Adversarial Labelling of Perturbed Samples	May 28, 2021	Adversarial RobustnessComputational Efficiency	—Unverified
On Linear Stability of SGD and Input-Smoothness of Neural Networks	May 27, 2021	Adversarial Robustness	CodeCode Available
Deep Repulsive Prototypes for Adversarial Robustness	May 26, 2021	Adversarial Robustness	—Unverified
Practical Convex Formulation of Robust One-hidden-layer Neural Network Training	May 25, 2021	Adversarial RobustnessBinary Classification	—Unverified
Adversarial Examples for k-Nearest Neighbor Classifiers Based on Higher-Order Voronoi Diagrams	May 21, 2021	Adversarial Robustness	—Unverified
Adversarial examples attack based on random warm restart mechanism and improved Nesterov momentum	May 10, 2021	Adversarial RobustnessDeep Learning	—Unverified
Efficiency-driven Hardware Optimization for Adversarially Robust Neural Networks	May 9, 2021	Adversarial Robustness	—Unverified
Dynamic Defense Approach for Adversarial Robustness in Deep Neural Networks via Stochastic Ensemble Smoothed Model	May 6, 2021	Adversarial RobustnessAttribute	—Unverified
A Finer Calibration Analysis for Adversarial Robustness	May 4, 2021	Adversarial RobustnessBIG-bench Machine Learning	—Unverified
On the Adversarial Robustness of Quantized Neural Networks	May 1, 2021	Adversarial RobustnessModel Compression	—Unverified
Impact of Spatial Frequency Based Constraints on Adversarial Robustness	Apr 26, 2021	Adversarial Robustness	—Unverified

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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