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 1376–1400 of 1746 papers

Title	Date	Tasks	Status
Output Perturbation for Differentially Private Convex Optimization: Faster and More General	Feb 9, 2021	Adversarial RobustnessFairness	—Unverified
SPADE: A Spectral Method for Black-Box Adversarial Robustness Evaluation	Feb 7, 2021	Adversarial RobustnessGraph Embedding	CodeCode Available
Optimal Transport as a Defense Against Adversarial Attacks	Feb 5, 2021	Adversarial RobustnessDomain Adaptation	CodeCode Available
Adversarial Robustness Study of Convolutional Neural Network for Lumbar Disk Shape Reconstruction from MR images	Feb 4, 2021	Adversarial RobustnessData Augmentation	CodeCode Available
Learning Diverse-Structured Networks for Adversarial Robustness	Feb 3, 2021	Adversarial Robustness	CodeCode Available
Recent Advances in Adversarial Training for Adversarial Robustness	Feb 2, 2021	Adversarial Robustness	—Unverified
Adversarial Learning with Cost-Sensitive Classes	Jan 29, 2021	Adversarial Robustness	—Unverified
Error Diffusion Halftoning Against Adversarial Examples	Jan 23, 2021	Adversarial RobustnessQuantization	CodeCode Available
Exploring Adversarial Robustness of Multi-Sensor Perception Systems in Self Driving	Jan 17, 2021	Adversarial RobustnessDenoising	—Unverified
Mining Data Impressions from Deep Models as Substitute for the Unavailable Training Data	Jan 15, 2021	Adversarial RobustnessContinual Learning	—Unverified
Adversarially Robust and Explainable Model Compression with On-Device Personalization for Text Classification	Jan 10, 2021	Adversarial RobustnessGeneral Classification	—Unverified
DiPSeN: Differentially Private Self-normalizing Neural Networks For Adversarial Robustness in Federated Learning	Jan 8, 2021	Adversarial RobustnessBIG-bench Machine Learning	—Unverified
The Effect of Prior Lipschitz Continuity on the Adversarial Robustness of Bayesian Neural Networks	Jan 7, 2021	Adversarial RobustnessVariational Inference	—Unverified
Adversarial Robustness by Design through Analog Computing and Synthetic Gradients	Jan 6, 2021	Adversarial RobustnessBinarization	CodeCode Available
Local Competition and Stochasticity for Adversarial Robustness in Deep Learning	Jan 4, 2021	Adversarial AttackAdversarial Robustness	—Unverified
Perceptual Adversarial Robustness: Generalizable Defenses Against Unforeseen Threat Models	Jan 1, 2021	Adversarial DefenseAdversarial Robustness	—Unverified
How Benign is Benign Overfitting ?	Jan 1, 2021	Adversarial RobustnessRepresentation Learning	—Unverified
Certifying Robustness of Graph Laplacian Based Semi-Supervised Learning	Jan 1, 2021	Adversarial Robustness	—Unverified
Manifold-aware Training: Increase Adversarial Robustness with Feature Clustering	Jan 1, 2021	Adversarial RobustnessClustering	—Unverified
Towards Robustness of Deep Neural Networks via Regularization	Jan 1, 2021	Adversarial AttackAdversarial Robustness	—Unverified
Hierarchical Binding in Convolutional Neural Networks Confers Adversarial Robustness	Jan 1, 2021	Adversarial Robustness	—Unverified
Self-supervised Adversarial Robustness for the Low-label, High-data Regime	Jan 1, 2021	Adversarial RobustnessSelf-Supervised Learning	—Unverified
GridMix: Strong regularization through local context mapping	Jan 1, 2021	Adversarial RobustnessData Augmentation	—Unverified
Robust Multi-Agent Reinforcement Learning Driven by Correlated Equilibrium	Jan 1, 2021	Adversarial RobustnessMulti-agent Reinforcement Learning	—Unverified
Generative Max-Mahalanobis Classifiers for Image Classification, Generation and More	Jan 1, 2021	Adversarial RobustnessClassification	CodeCode Available

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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