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 926–950 of 1746 papers

Title	Date	Tasks	Status	Hype
Fundamental Limits in Formal Verification of Message-Passing Neural Networks	Jun 10, 2022	Adversarial RobustnessClassification	—Unverified	0
CARLA-GeAR: a Dataset Generator for a Systematic Evaluation of Adversarial Robustness of Vision Models	Jun 9, 2022	Adversarial DefenseAdversarial Robustness	CodeCode Available	1
Wavelet Regularization Benefits Adversarial Training	Jun 8, 2022	Adversarial Robustness	CodeCode Available	0
LADDER: Latent Boundary-guided Adversarial Training	Jun 8, 2022	Adversarial Robustness	CodeCode Available	0
Building Robust Ensembles via Margin Boosting	Jun 7, 2022	Adversarial Robustness	CodeCode Available	0
Improving Adversarial Robustness by Putting More Regularizations on Less Robust Samples	Jun 7, 2022	Adversarial Robustness	CodeCode Available	0
A Robust Backpropagation-Free Framework for Images	Jun 3, 2022	Adversarial Robustness	CodeCode Available	0
Adaptive Adversarial Training to Improve Adversarial Robustness of DNNs for Medical Image Segmentation and Detection	Jun 2, 2022	Adversarial RobustnessImage Segmentation	—Unverified	0
The robust way to stack and bag: the local Lipschitz way	Jun 1, 2022	Adversarial Robustness	—Unverified	0
Sequential Bayesian Neural Subnetwork Ensembles	Jun 1, 2022	Adversarial RobustnessOut-of-Distribution Detection	—Unverified	0
Level Up with ML Vulnerability Identification: Leveraging Domain Constraints in Feature Space for Robust Android Malware Detection	May 30, 2022	Adversarial RobustnessAndroid Malware Detection	CodeCode Available	0
CalFAT: Calibrated Federated Adversarial Training with Label Skewness	May 30, 2022	Adversarial RobustnessFederated Learning	CodeCode Available	0
Exposing Fine-Grained Adversarial Vulnerability of Face Anti-Spoofing Models	May 30, 2022	Adversarial AttackAdversarial Robustness	—Unverified	0
Semi-supervised Semantics-guided Adversarial Training for Trajectory Prediction	May 27, 2022	Adversarial RobustnessDecision Making	—Unverified	0
Functional Network: A Novel Framework for Interpretability of Deep Neural Networks	May 24, 2022	Adversarial RobustnessTopological Data Analysis	—Unverified	0
Squeeze Training for Adversarial Robustness	May 23, 2022	Adversarial Robustness	CodeCode Available	0
Hierarchical Distribution-Aware Testing of Deep Learning	May 17, 2022	Adversarial RobustnessData Compression	CodeCode Available	0
Exploiting the Relationship Between Kendall's Rank Correlation and Cosine Similarity for Attribution Protection	May 15, 2022	Adversarial Robustness	—Unverified	0
Evaluating Membership Inference Through Adversarial Robustness	May 14, 2022	Adversarial RobustnessDeep Learning	CodeCode Available	0
Sibylvariant Transformations for Robust Text Classification	May 10, 2022	Adversarial RobustnessDefect Detection	CodeCode Available	0
Structural Extensions of Basis Pursuit: Guarantees on Adversarial Robustness	May 5, 2022	Adversarial Robustness	—Unverified	0
Can collaborative learning be private, robust and scalable?	May 5, 2022	Adversarial RobustnessFederated Learning	—Unverified	0
CE-based white-box adversarial attacks will not work using super-fitting	May 4, 2022	Adversarial AttackAdversarial Robustness	—Unverified	0
Towards Theoretical Analysis of Transformation Complexity of ReLU DNNs	May 4, 2022	Adversarial RobustnessDisentanglement	CodeCode Available	0
FedNest: Federated Bilevel, Minimax, and Compositional Optimization	May 4, 2022	Adversarial RobustnessHyperparameter Optimization	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