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 251–275 of 1746 papers

Title	Date	Tasks	Status	Hype
Make Sure You're Unsure: A Framework for Verifying Probabilistic Specifications	Feb 18, 2021	Adversarial RobustnessOut of Distribution (OOD) Detection	CodeCode Available	1
CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection	Feb 10, 2021	Adversarial Robustnessfeature selection	CodeCode Available	1
Towards Adversarial Robustness of Bayesian Neural Network through Hierarchical Variational Inference	Jan 1, 2021	Adversarial DefenseAdversarial Robustness	CodeCode Available	1
Better Robustness by More Coverage: Adversarial Training with Mixup Augmentation for Robust Fine-tuning	Dec 31, 2020	Adversarial RobustnessData Augmentation	CodeCode Available	1
Composite Adversarial Attacks	Dec 10, 2020	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Using Feature Alignment Can Improve Clean Average Precision and Adversarial Robustness in Object Detection	Dec 8, 2020	2D Object DetectionAdversarial Attack	CodeCode Available	1
On the Trade-off between Adversarial and Backdoor Robustness	Dec 1, 2020	Adversarial Robustness	CodeCode Available	1
Regularization with Latent Space Virtual Adversarial Training	Nov 26, 2020	Adversarial Robustnessimage-classification	CodeCode Available	1
A Reputation Mechanism Is All You Need: Collaborative Fairness and Adversarial Robustness in Federated Learning	Nov 20, 2020	Adversarial DefenseAdversarial Robustness	CodeCode Available	1
SHIELD: Defending Textual Neural Networks against Multiple Black-Box Adversarial Attacks with Stochastic Multi-Expert Patcher	Nov 17, 2020	Adversarial Robustness	CodeCode Available	1
Adversarial Image Color Transformations in Explicit Color Filter Space	Nov 12, 2020	Adversarial Robustness	CodeCode Available	1
Robust Pre-Training by Adversarial Contrastive Learning	Oct 26, 2020	Adversarial RobustnessContrastive Learning	CodeCode Available	1
RobustBench: a standardized adversarial robustness benchmark	Oct 19, 2020	Adversarial RobustnessBenchmarking	CodeCode Available	1
Shape-Texture Debiased Neural Network Training	Oct 12, 2020	Adversarial RobustnessData Augmentation	CodeCode Available	1
Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples	Oct 7, 2020	Adversarial Robustness	CodeCode Available	1
Bag of Tricks for Adversarial Training	Oct 1, 2020	Adversarial RobustnessBenchmarking	CodeCode Available	1
Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup	Sep 15, 2020	Adversarial RobustnessImage Classification	CodeCode Available	1
Adversarial Attack and Defense Strategies for Deep Speaker Recognition Systems	Aug 18, 2020	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Neural Networks with Recurrent Generative Feedback	Jul 17, 2020	Adversarial Robustness	CodeCode Available	1
Certifiably Adversarially Robust Detection of Out-of-Distribution Data	Jul 16, 2020	Adversarial RobustnessOut of Distribution (OOD) Detection	CodeCode Available	1
Multitask Learning Strengthens Adversarial Robustness	Jul 14, 2020	Adversarial DefenseAdversarial Robustness	CodeCode Available	1
Understanding Object Detection Through An Adversarial Lens	Jul 11, 2020	Adversarial RobustnessAutonomous Vehicles	CodeCode Available	1
Improving Adversarial Robustness by Enforcing Local and Global Compactness	Jul 10, 2020	Adversarial RobustnessClustering	CodeCode Available	1
RobFR: Benchmarking Adversarial Robustness on Face Recognition	Jul 8, 2020	Adversarial RobustnessBenchmarking	CodeCode Available	1
Proper Network Interpretability Helps Adversarial Robustness in Classification	Jun 26, 2020	Adversarial RobustnessClassification	CodeCode Available	1

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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