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
The King is Naked: on the Notion of Robustness for Natural Language Processing	Dec 13, 2021	Adversarial Robustness	CodeCode Available
Analyzing and Improving the Robustness of Tabular Classifiers using Counterfactual Explanations	Dec 13, 2021	Adversarial Robustnesscounterfactual	CodeCode Available
Spatial-Temporal-Fusion BNN: Variational Bayesian Feature Layer	Dec 12, 2021	Adversarial RobustnessUncertainty Quantification	—Unverified
On Adversarial Robustness of Point Cloud Semantic Segmentation	Dec 11, 2021	Adversarial RobustnessAutonomous Driving	CodeCode Available
Robustness Certificates for Implicit Neural Networks: A Mixed Monotone Contractive Approach	Dec 10, 2021	Adversarial Robustness	—Unverified
SNEAK: Synonymous Sentences-Aware Adversarial Attack on Natural Language Video Localization	Dec 8, 2021	Adversarial AttackAdversarial Robustness	—Unverified
On the Existence of the Adversarial Bayes Classifier (Extended Version)	Dec 3, 2021	Adversarial Robustness	—Unverified
Is Approximation Universally Defensive Against Adversarial Attacks in Deep Neural Networks?	Dec 2, 2021	Adversarial Robustness	—Unverified
Adversarial Robustness of Deep Reinforcement Learning based Dynamic Recommender Systems	Dec 2, 2021	Adversarial Robustnesscounterfactual	—Unverified
Formalizing Generalization and Adversarial Robustness of Neural Networks to Weight Perturbations	Dec 1, 2021	Adversarial RobustnessModel Compression	—Unverified
Towards Stable and Robust AdderNets	Dec 1, 2021	Adversarial Robustness	—Unverified
Push Stricter to Decide Better: A Class-Conditional Feature Adaptive Framework for Improving Adversarial Robustness	Dec 1, 2021	Adversarial Robustness	CodeCode Available
Certified Adversarial Defenses Meet Out-of-Distribution Corruptions: Benchmarking Robustness and Simple Baselines	Dec 1, 2021	Adversarial RobustnessBenchmarking	—Unverified
Adversarially Robust 3D Point Cloud Recognition Using Self-Supervisions	Dec 1, 2021	Adversarial RobustnessAutonomous Driving	—Unverified
A Systematic Review of Robustness in Deep Learning for Computer Vision: Mind the gap?	Dec 1, 2021	Adversarial RobustnessData Augmentation	—Unverified
Clustering Effect of Adversarial Robust Models	Dec 1, 2021	Adversarial RobustnessClustering	—Unverified
On the Existence of The Adversarial Bayes Classifier	Dec 1, 2021	Adversarial Robustness	—Unverified
Exponential Separation between Two Learning Models and Adversarial Robustness	Dec 1, 2021	Adversarial RobustnessPAC learning	—Unverified
Adversarial Robustness without Adversarial Training: A Teacher-Guided Curriculum Learning Approach	Dec 1, 2021	Adversarial Robustness	—Unverified
Robustness between the worst and average case	Dec 1, 2021	Adversarial Robustness	CodeCode Available
Clustering Effect of (Linearized) Adversarial Robust Models	Nov 25, 2021	Adversarial RobustnessClustering	CodeCode Available
Adversarial Examples on Segmentation Models Can be Easy to Transfer	Nov 22, 2021	Adversarial RobustnessAttribute	—Unverified
Revisiting Adversarial Robustness of Classifiers With a Reject Option	Nov 22, 2021	Adversarial Robustnessimage-classification	—Unverified
Heterogeneous Architecture Search Approach within Adversarial Dynamic Defense Framework	Nov 22, 2021	Adversarial AttackAdversarial Robustness	—Unverified
The Diversity Metrics of Sub-models based on SVD of Jacobians for Ensembles Adversarial Robustness	Nov 22, 2021	Adversarial RobustnessAttribute	—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