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 826–850 of 1746 papers

Title	Date	Tasks	Status	Hype
Pruning Adversarially Robust Neural Networks without Adversarial Examples	Oct 9, 2022	Adversarial Robustness	CodeCode Available	1
Symmetry Defense Against CNN Adversarial Perturbation Attacks	Oct 8, 2022	Adversarial RobustnessAutonomous Vehicles	—Unverified	0
Towards Out-of-Distribution Adversarial Robustness	Oct 6, 2022	Adversarial Robustness	CodeCode Available	0
On Adversarial Robustness of Deep Image Deblurring	Oct 5, 2022	Adversarial RobustnessDeblurring	—Unverified	0
Strength-Adaptive Adversarial Training	Oct 4, 2022	Adversarial RobustnessScheduling	—Unverified	0
Push-Pull: Characterizing the Adversarial Robustness for Audio-Visual Active Speaker Detection	Oct 3, 2022	Active Speaker DetectionAdversarial Robustness	—Unverified	0
Adaptive Smoothness-weighted Adversarial Training for Multiple Perturbations with Its Stability Analysis	Oct 2, 2022	Adversarial Robustness	CodeCode Available	0
Understanding Adversarial Robustness Against On-manifold Adversarial Examples	Oct 2, 2022	Adversarial Robustness	CodeCode Available	0
On the tightness of linear relaxation based robustness certification methods	Oct 1, 2022	Adversarial Robustness	—Unverified	0
Improving Robustness with Adaptive Weight Decay	Sep 30, 2022	Adversarial Robustness	CodeCode Available	0
Adversarial Robustness of Representation Learning for Knowledge Graphs	Sep 30, 2022	Adversarial RobustnessData Poisoning	CodeCode Available	1
Generalizability of Adversarial Robustness Under Distribution Shifts	Sep 29, 2022	Adversarial RobustnessDomain Generalization	—Unverified	0
Inducing Data Amplification Using Auxiliary Datasets in Adversarial Training	Sep 27, 2022	Adversarial Robustness	CodeCode Available	0
Fair Robust Active Learning by Joint Inconsistency	Sep 22, 2022	Active LearningAdversarial Attack	—Unverified	0
Audit and Improve Robustness of Private Neural Networks on Encrypted Data	Sep 20, 2022	Adversarial RobustnessPrivacy Preserving	—Unverified	0
AdvDO: Realistic Adversarial Attacks for Trajectory Prediction	Sep 19, 2022	Adversarial AttackAdversarial Robustness	—Unverified	0
Characterizing Internal Evasion Attacks in Federated Learning	Sep 17, 2022	Adversarial RobustnessFederated Learning	CodeCode Available	1
Towards Bridging the Performance Gaps of Joint Energy-based Models	Sep 16, 2022	Adversarial RobustnessData Augmentation	CodeCode Available	0
Explicit Tradeoffs between Adversarial and Natural Distributional Robustness	Sep 15, 2022	Adversarial Robustness	—Unverified	0
Part-Based Models Improve Adversarial Robustness	Sep 15, 2022	Adversarial Robustness	CodeCode Available	1
A Light Recipe to Train Robust Vision Transformers	Sep 15, 2022	Adversarial RobustnessData Augmentation	CodeCode Available	1
On the interplay of adversarial robustness and architecture components: patches, convolution and attention	Sep 14, 2022	Adversarial Robustnessimage-classification	—Unverified	0
Correlation Information Bottleneck: Towards Adapting Pretrained Multimodal Models for Robust Visual Question Answering	Sep 14, 2022	Adversarial RobustnessQuestion Answering	—Unverified	0
PointACL:Adversarial Contrastive Learning for Robust Point Clouds Representation under Adversarial Attack	Sep 14, 2022	3D ClassificationAdversarial Attack	CodeCode Available	0
Robust Transferable Feature Extractors: Learning to Defend Pre-Trained Networks Against White Box Adversaries	Sep 14, 2022	Adversarial Robustness	—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