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 276–300 of 1746 papers

Title	Date	Tasks	Status	Hype
Smooth Adversarial Training	Jun 25, 2020	Adversarial DefenseAdversarial Robustness	CodeCode Available	1
Imbalanced Gradients: A Subtle Cause of Overestimated Adversarial Robustness	Jun 24, 2020	Adversarial Robustness	CodeCode Available	1
Perceptual Adversarial Robustness: Defense Against Unseen Threat Models	Jun 22, 2020	Adversarial DefenseAdversarial Robustness	CodeCode Available	1
Achieving robustness in classification using optimal transport with hinge regularization	Jun 11, 2020	Adversarial RobustnessBinary Classification	CodeCode Available	1
A Self-supervised Approach for Adversarial Robustness	Jun 8, 2020	Adversarial RobustnessGeneral Classification	CodeCode Available	1
Consistency Regularization for Certified Robustness of Smoothed Classifiers	Jun 7, 2020	Adversarial Robustness	CodeCode Available	1
Benchmarking Adversarial Robustness on Image Classification	Jun 1, 2020	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Adversarial Robustness of Deep Convolutional Candlestick Learner	May 29, 2020	Adversarial Robustness	CodeCode Available	1
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
Improve robustness of DNN for ECG signal classification:a noise-to-signal ratio perspective	May 18, 2020	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Evaluating and Improving Adversarial Robustness of Machine Learning-Based Network Intrusion Detectors	May 15, 2020	Adversarial RobustnessBIG-bench Machine Learning	CodeCode Available	1
Efficient Exact Verification of Binarized Neural Networks	May 7, 2020	Adversarial Robustness	CodeCode Available	1
Enhancing Intrinsic Adversarial Robustness via Feature Pyramid Decoder	May 6, 2020	Adversarial RobustnessDecoder	CodeCode Available	1
Improving Adversarial Robustness Requires Revisiting Misclassified Examples	May 1, 2020	Adversarial Robustness	CodeCode Available	1
Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness	Apr 30, 2020	Adversarial Robustness	CodeCode Available	1
Adversarial Attack on Deep Learning-Based Splice Localization	Apr 17, 2020	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Adversarial Weight Perturbation Helps Robust Generalization	Apr 13, 2020	Adversarial Robustness	CodeCode Available	1
Towards Achieving Adversarial Robustness by Enforcing Feature Consistency Across Bit Planes	Apr 1, 2020	Adversarial Robustness	CodeCode Available	1
Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning	Mar 28, 2020	Adversarial Robustness	CodeCode Available	1
Inherent Adversarial Robustness of Deep Spiking Neural Networks: Effects of Discrete Input Encoding and Non-Linear Activations	Mar 23, 2020	Adversarial RobustnessAttribute	CodeCode Available	1
Adversarial Robustness on In- and Out-Distribution Improves Explainability	Mar 20, 2020	Adversarial Robustnessimage-classification	CodeCode Available	1
Toward Adversarial Robustness via Semi-supervised Robust Training	Mar 16, 2020	Adversarial DefenseAdversarial Robustness	CodeCode Available	1
ARAE: Adversarially Robust Training of Autoencoders Improves Novelty Detection	Mar 12, 2020	Adversarial RobustnessNovelty Detection	CodeCode Available	1
Adversarial Machine Learning: Bayesian Perspectives	Mar 7, 2020	Adversarial RobustnessBIG-bench Machine Learning	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