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 1176–1200 of 1746 papers

Title	Date	Tasks	Status	Hype
GradDiv: Adversarial Robustness of Randomized Neural Networks via Gradient Diversity Regularization	Jul 6, 2021	Adversarial RobustnessDiversity	—Unverified	0
Generalization Error Analysis of Neural networks with Gradient Based Regularization	Jul 6, 2021	Adversarial Robustnessimage-classification	—Unverified	0
Adversarial Robustness of Probabilistic Network Embedding for Link Prediction	Jul 5, 2021	Adversarial RobustnessLink Prediction	—Unverified	0
Adversarial Robustness of Streaming Algorithms through Importance Sampling	Jun 28, 2021	Adversarial RobustnessClustering	—Unverified	0
RAILS: A Robust Adversarial Immune-inspired Learning System	Jun 27, 2021	Adversarial DefenseAdversarial Robustness	CodeCode Available	1
Multi-stage Optimization based Adversarial Training	Jun 26, 2021	Adversarial Robustness	—Unverified	0
On the (Un-)Avoidability of Adversarial Examples	Jun 24, 2021	Adversarial RobustnessData Augmentation	—Unverified	0
Long-term Cross Adversarial Training: A Robust Meta-learning Method for Few-shot Classification Tasks	Jun 22, 2021	Adversarial RobustnessClassification	CodeCode Available	0
On Adversarial Robustness of Synthetic Code Generation	Jun 22, 2021	Adversarial RobustnessCode Generation	—Unverified	0
NCIS: Neural Contextual Iterative Smoothing for Purifying Adversarial Perturbations	Jun 22, 2021	Adversarial DefenseAdversarial Robustness	—Unverified	0
Policy Smoothing for Provably Robust Reinforcement Learning	Jun 21, 2021	Adversarial Robustnessimage-classification	—Unverified	0
SmoothMix: Training Confidence-calibrated Smoothed Classifiers for Certified Adversarial Robustness	Jun 18, 2021	Adversarial Robustness	—Unverified	0
Attacking Graph Classification via Bayesian Optimisation	Jun 18, 2021	Adversarial RobustnessBayesian Optimisation	—Unverified	0
Helper-based Adversarial Training: Reducing Excessive Margin to Achieve a Better Accuracy vs. Robustness Trade-off	Jun 18, 2021	Adversarial DefenseAdversarial Robustness	CodeCode Available	1
Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples	Jun 18, 2021	Adversarial Robustness	CodeCode Available	3
Residual Error: a New Performance Measure for Adversarial Robustness	Jun 18, 2021	Adversarial Robustnessimage-classification	—Unverified	0
Less is More: Feature Selection for Adversarial Robustness with Compressive Counter-Adversarial Attacks	Jun 18, 2021	Adversarial Robustnessfeature selection	—Unverified	0
Federated Robustness Propagation: Sharing Robustness in Heterogeneous Federated Learning	Jun 18, 2021	Adversarial RobustnessFederated Learning	CodeCode Available	1
Analyzing Adversarial Robustness of Deep Neural Networks in Pixel Space: a Semantic Perspective	Jun 18, 2021	Adversarial Robustness	—Unverified	0
Adversarial Visual Robustness by Causal Intervention	Jun 17, 2021	Adversarial Robustness	CodeCode Available	1
Algorithmic Bias and Data Bias: Understanding the Relation between Distributionally Robust Optimization and Data Curation	Jun 17, 2021	Adversarial RobustnessFairness	—Unverified	0
Towards Adversarial Robustness via Transductive Learning	Jun 15, 2021	Adversarial RobustnessBilevel Optimization	—Unverified	0
Probabilistic Margins for Instance Reweighting in Adversarial Training	Jun 15, 2021	Adversarial Robustness	CodeCode Available	1
ATRAS: Adversarially Trained Robust Architecture Search	Jun 13, 2021	Adversarial Robustness	—Unverified	0
Understanding the Interplay between Privacy and Robustness in Federated Learning	Jun 13, 2021	Adversarial RobustnessFederated 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