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 576–600 of 1746 papers

Title	Date	Tasks	Status	Hype
Homophily-Driven Sanitation View for Robust Graph Contrastive Learning	Jul 24, 2023	Adversarial RobustnessContrastive Learning	—Unverified	0
HybridAugment++: Unified Frequency Spectra Perturbations for Model Robustness	Jul 21, 2023	Adversarial RobustnessData Augmentation	CodeCode Available	1
A Holistic Assessment of the Reliability of Machine Learning Systems	Jul 20, 2023	Adversarial RobustnessOut-of-Distribution Detection	—Unverified	0
Omnipotent Adversarial Training in the Wild	Jul 14, 2023	Adversarial Robustness	CodeCode Available	0
Mitigating Adversarial Vulnerability through Causal Parameter Estimation by Adversarial Double Machine Learning	Jul 14, 2023	Adversarial Robustnessparameter estimation	CodeCode Available	1
Min-Max Optimization under Delays	Jul 13, 2023	Adversarial RobustnessStochastic Optimization	—Unverified	0
Function-Space Regularization for Deep Bayesian Classification	Jul 12, 2023	Adversarial RobustnessClassification	—Unverified	0
Enhancing Adversarial Robustness via Score-Based Optimization	Jul 10, 2023	Adversarial DefenseAdversarial Robustness	CodeCode Available	1
A unifying framework for differentially private quantum algorithms	Jul 10, 2023	Adversarial Robustness	—Unverified	0
A Theoretical Perspective on Subnetwork Contributions to Adversarial Robustness	Jul 7, 2023	Adversarial Robustness	—Unverified	0
Transgressing the boundaries: towards a rigorous understanding of deep learning and its (non-)robustness	Jul 5, 2023	Adversarial RobustnessLearning Theory	—Unverified	0
Kernels, Data & Physics	Jul 5, 2023	Adversarial RobustnessInductive Bias	—Unverified	0
On the Adversarial Robustness of Generative Autoencoders in the Latent Space	Jul 5, 2023	Adversarial Robustnesscompressed sensing	—Unverified	0
The Importance of Robust Features in Mitigating Catastrophic Forgetting	Jun 29, 2023	Adversarial RobustnessContinual Learning	—Unverified	0
Mitigating Accuracy-Robustness Trade-off via Balanced Multi-Teacher Adversarial Distillation	Jun 28, 2023	Adversarial RobustnessKnowledge Distillation	CodeCode Available	1
A Survey on Out-of-Distribution Evaluation of Neural NLP Models	Jun 27, 2023	Adversarial RobustnessDomain Generalization	—Unverified	0
Advancing Adversarial Training by Injecting Booster Signal	Jun 27, 2023	Adversarial Robustness	—Unverified	0
Robust Proxy: Improving Adversarial Robustness by Robust Proxy Learning	Jun 27, 2023	Adversarial Robustness	—Unverified	0
Computational Asymmetries in Robust Classification	Jun 25, 2023	Adversarial RobustnessClassification	CodeCode Available	0
Robust Spatiotemporal Traffic Forecasting with Reinforced Dynamic Adversarial Training	Jun 25, 2023	Adversarial RobustnessKnowledge Distillation	CodeCode Available	1
A Spectral Perspective towards Understanding and Improving Adversarial Robustness	Jun 25, 2023	Adversarial Robustness	—Unverified	0
On Evaluating the Adversarial Robustness of Semantic Segmentation Models	Jun 25, 2023	Adversarial Robustnessimage-classification	—Unverified	0
Enhancing Adversarial Training via Reweighting Optimization Trajectory	Jun 25, 2023	Adversarial Robustness	CodeCode Available	0
Similarity Preserving Adversarial Graph Contrastive Learning	Jun 24, 2023	Adversarial RobustnessContrastive Learning	CodeCode Available	1
Adversarial Robustness Certification for Bayesian Neural Networks	Jun 23, 2023	Adversarial RobustnessCollision Avoidance	CodeCode Available	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