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 1051–1075 of 1746 papers

Title	Date	Tasks	Status	Hype
A Systematic Review of Robustness in Deep Learning for Computer Vision: Mind the gap?	Dec 1, 2021	Adversarial RobustnessData Augmentation	—Unverified	0
Push Stricter to Decide Better: A Class-Conditional Feature Adaptive Framework for Improving Adversarial Robustness	Dec 1, 2021	Adversarial Robustness	CodeCode Available	0
Clustering Effect of Adversarial Robust Models	Dec 1, 2021	Adversarial RobustnessClustering	—Unverified	0
Robustness between the worst and average case	Dec 1, 2021	Adversarial Robustness	CodeCode Available	0
Exponential Separation between Two Learning Models and Adversarial Robustness	Dec 1, 2021	Adversarial RobustnessPAC learning	—Unverified	0
Adversarial Robustness without Adversarial Training: A Teacher-Guided Curriculum Learning Approach	Dec 1, 2021	Adversarial Robustness	—Unverified	0
Towards a Unified Game-Theoretic View of Adversarial Perturbations and Robustness	Dec 1, 2021	Adversarial Robustness	CodeCode Available	1
On the Existence of The Adversarial Bayes Classifier	Dec 1, 2021	Adversarial Robustness	—Unverified	0
Adversarially Robust 3D Point Cloud Recognition Using Self-Supervisions	Dec 1, 2021	Adversarial RobustnessAutonomous Driving	—Unverified	0
Adversarial Attacks on Graph Classifiers via Bayesian Optimisation	Dec 1, 2021	Adversarial RobustnessBayesian Optimisation	CodeCode Available	1
Formalizing Generalization and Adversarial Robustness of Neural Networks to Weight Perturbations	Dec 1, 2021	Adversarial RobustnessModel Compression	—Unverified	0
Towards Stable and Robust AdderNets	Dec 1, 2021	Adversarial Robustness	—Unverified	0
ExCon: Explanation-driven Supervised Contrastive Learning for Image Classification	Nov 28, 2021	Adversarial RobustnessClassification	CodeCode Available	1
Clustering Effect of (Linearized) Adversarial Robust Models	Nov 25, 2021	Adversarial RobustnessClustering	CodeCode Available	0
Provable Defense Against Clustering Attacks on 3D Point Clouds	Nov 22, 2021	Adversarial RobustnessClustering	—Unverified	0
The Diversity Metrics of Sub-models based on SVD of Jacobians for Ensembles Adversarial Robustness	Nov 22, 2021	Adversarial RobustnessAttribute	—Unverified	0
Heterogeneous Architecture Search Approach within Adversarial Dynamic Defense Framework	Nov 22, 2021	Adversarial AttackAdversarial Robustness	—Unverified	0
Revisiting Adversarial Robustness of Classifiers With a Reject Option	Nov 22, 2021	Adversarial Robustnessimage-classification	—Unverified	0
Adversarial Examples on Segmentation Models Can be Easy to Transfer	Nov 22, 2021	Adversarial RobustnessAttribute	—Unverified	0
Flooding-X: Improving BERT's Resistance to Adversarial Attacks via Loss-Restricted Fine-Tuning	Nov 16, 2021	Adversarial Robustnesstext-classification	—Unverified	0
Removing Out-of-Distribution Data Improves Adversarial Robustness	Nov 16, 2021	Adversarial Robustness	—Unverified	0
Robust and Accurate Object Detection via Self-Knowledge Distillation	Nov 14, 2021	Adversarial RobustnessKnowledge Distillation	CodeCode Available	0
Neural Population Geometry Reveals the Role of Stochasticity in Robust Perception	Nov 12, 2021	Adversarial Robustness	CodeCode Available	0
Are Transformers More Robust Than CNNs?	Nov 10, 2021	Adversarial Robustness	CodeCode Available	1
Tightening the Approximation Error of Adversarial Risk with Auto Loss Function Search	Nov 9, 2021	Adversarial RobustnessAutoML	—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