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 1476–1500 of 1746 papers

Title	Date	Tasks	Status	Hype
Debona: Decoupled Boundary Network Analysis for Tighter Bounds and Faster Adversarial Robustness Proofs	Jun 16, 2020	Adversarial Robustness	CodeCode Available	0
Improving Adversarial Robustness via Unlabeled Out-of-Domain Data	Jun 15, 2020	Adversarial RobustnessData Augmentation	—Unverified	0
On Saliency Maps and Adversarial Robustness	Jun 14, 2020	Adversarial Robustness	—Unverified	0
Achieving robustness in classification using optimal transport with hinge regularization	Jun 11, 2020	Adversarial RobustnessBinary Classification	CodeCode Available	1
Deterministic Gaussian Averaged Neural Networks	Jun 10, 2020	Adversarial Robustnessregression	CodeCode Available	0
A Self-supervised Approach for Adversarial Robustness	Jun 8, 2020	Adversarial RobustnessGeneral Classification	CodeCode Available	1
Adversarial Feature Desensitization	Jun 8, 2020	Adversarial RobustnessDomain Adaptation	CodeCode Available	0
The Lipschitz Constant of Self-Attention	Jun 8, 2020	Adversarial RobustnessLanguage Modelling	—Unverified	0
Consistency Regularization for Certified Robustness of Smoothed Classifiers	Jun 7, 2020	Adversarial Robustness	CodeCode Available	1
Robust Face Verification via Disentangled Representations	Jun 5, 2020	Adversarial RobustnessFace Verification	CodeCode Available	0
UFO-BLO: Unbiased First-Order Bilevel Optimization	Jun 5, 2020	Adversarial RobustnessBilevel Optimization	—Unverified	0
Benchmarking Adversarial Robustness on Image Classification	Jun 1, 2020	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Rethinking Empirical Evaluation of Adversarial Robustness Using First-Order Attack Methods	Jun 1, 2020	Adversarial RobustnessBenchmarking	CodeCode Available	0
Adversarial Robustness of Deep Convolutional Candlestick Learner	May 29, 2020	Adversarial Robustness	CodeCode Available	1
Investigating Vulnerability to Adversarial Examples on Multimodal Data Fusion in Deep Learning	May 22, 2020	Adversarial AttackAdversarial Robustness	—Unverified	0
Robust Ensemble Model Training via Random Layer Sampling Against Adversarial Attack	May 21, 2020	Adversarial AttackAdversarial Robustness	—Unverified	0
Revisiting Role of Autoencoders in Adversarial Settings	May 21, 2020	Adversarial DefenseAdversarial Robustness	—Unverified	0
Model-Based Robust Deep Learning: Generalizing to Natural, Out-of-Distribution Data	May 20, 2020	Adversarial RobustnessDeep Learning	CodeCode Available	1
On Intrinsic Dataset Properties for Adversarial Machine Learning	May 19, 2020	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Increasing-Margin Adversarial (IMA) Training to Improve Adversarial Robustness of Neural Networks	May 19, 2020	Adversarial RobustnessGeneral Classification	CodeCode Available	0
Improve robustness of DNN for ECG signal classification:a noise-to-signal ratio perspective	May 18, 2020	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Toward Adversarial Robustness by Diversity in an Ensemble of Specialized Deep Neural Networks	May 17, 2020	Adversarial RobustnessDiversity	—Unverified	0
Evaluating and Improving Adversarial Robustness of Machine Learning-Based Network Intrusion Detectors	May 15, 2020	Adversarial RobustnessBIG-bench Machine Learning	CodeCode Available	1
Towards Assessment of Randomized Smoothing Mechanisms for Certifying Adversarial Robustness	May 15, 2020	Adversarial Robustness	—Unverified	0
Class-Aware Domain Adaptation for Improving Adversarial Robustness	May 10, 2020	Adversarial AttackAdversarial Defense	—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