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 1026–1050 of 1746 papers

Title	Date	Tasks	Status	Hype
Improving the Behaviour of Vision Transformers with Token-consistent Stochastic Layers	Dec 30, 2021	Adversarial RobustnessTransfer Learning	—Unverified	0
Associative Adversarial Learning Based on Selective Attack	Dec 28, 2021	Adversarial RobustnessFew-Shot Learning	—Unverified	0
Perlin Noise Improve Adversarial Robustness	Dec 26, 2021	Adversarial Robustness	—Unverified	0
Understanding and Measuring Robustness of Multimodal Learning	Dec 22, 2021	Adversarial Robustness	—Unverified	0
How Should Pre-Trained Language Models Be Fine-Tuned Towards Adversarial Robustness?	Dec 22, 2021	Adversarial RobustnessNatural Language Inference	CodeCode Available	1
On the Adversarial Robustness of Causal Algorithmic Recourse	Dec 21, 2021	Adversarial RobustnessDecision Making	CodeCode Available	0
Improving Robustness with Image Filtering	Dec 21, 2021	Adversarial RobustnessData Augmentation	—Unverified	0
Certified Federated Adversarial Training	Dec 20, 2021	Adversarial RobustnessFederated Learning	—Unverified	0
Analyzing and Improving the Robustness of Tabular Classifiers using Counterfactual Explanations	Dec 13, 2021	Adversarial Robustnesscounterfactual	CodeCode Available	0
The King is Naked: on the Notion of Robustness for Natural Language Processing	Dec 13, 2021	Adversarial Robustness	CodeCode Available	0
Spatial-Temporal-Fusion BNN: Variational Bayesian Feature Layer	Dec 12, 2021	Adversarial RobustnessUncertainty Quantification	—Unverified	0
On Adversarial Robustness of Point Cloud Semantic Segmentation	Dec 11, 2021	Adversarial RobustnessAutonomous Driving	CodeCode Available	0
Robustness Certificates for Implicit Neural Networks: A Mixed Monotone Contractive Approach	Dec 10, 2021	Adversarial Robustness	—Unverified	0
PixMix: Dreamlike Pictures Comprehensively Improve Safety Measures	Dec 9, 2021	Adversarial RobustnessAnomaly Detection	CodeCode Available	1
Segment and Complete: Defending Object Detectors against Adversarial Patch Attacks with Robust Patch Detection	Dec 8, 2021	Adversarial Attack DetectionAdversarial Defense	CodeCode Available	1
SNEAK: Synonymous Sentences-Aware Adversarial Attack on Natural Language Video Localization	Dec 8, 2021	Adversarial AttackAdversarial Robustness	—Unverified	0
Decision-based Black-box Attack Against Vision Transformers via Patch-wise Adversarial Removal	Dec 7, 2021	Adversarial RobustnessSensitivity	CodeCode Available	1
Stochastic Local Winner-Takes-All Networks Enable Profound Adversarial Robustness	Dec 5, 2021	Adversarial AttackAdversarial Defense	CodeCode Available	1
On the Existence of the Adversarial Bayes Classifier (Extended Version)	Dec 3, 2021	Adversarial Robustness	—Unverified	0
Is RobustBench/AutoAttack a suitable Benchmark for Adversarial Robustness?	Dec 2, 2021	Adversarial Attack DetectionAdversarial Robustness	CodeCode Available	1
Is Approximation Universally Defensive Against Adversarial Attacks in Deep Neural Networks?	Dec 2, 2021	Adversarial Robustness	—Unverified	0
A Unified Framework for Adversarial Attack and Defense in Constrained Feature Space	Dec 2, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Training Efficiency and Robustness in Deep Learning	Dec 2, 2021	Adversarial RobustnessDeep Learning	CodeCode Available	1
Adversarial Robustness of Deep Reinforcement Learning based Dynamic Recommender Systems	Dec 2, 2021	Adversarial Robustnesscounterfactual	—Unverified	0
Certified Adversarial Defenses Meet Out-of-Distribution Corruptions: Benchmarking Robustness and Simple Baselines	Dec 1, 2021	Adversarial RobustnessBenchmarking	—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