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 1576–1600 of 1746 papers

Title	Date	Tasks	Status	Hype
Universal Adversarial Robustness of Texture and Shape-Biased Models	Nov 23, 2019	Adversarial RobustnessImage Classification	CodeCode Available	1
Verifiability and Predictability: Interpreting Utilities of Network Architectures for Point Cloud Processing	Nov 20, 2019	Adversarial Robustness	CodeCode Available	0
Adversarial Robustness of Flow-Based Generative Models	Nov 20, 2019	Adversarial Robustness	—Unverified	0
AdvKnn: Adversarial Attacks On K-Nearest Neighbor Classifiers With Approximate Gradients	Nov 15, 2019	Adversarial Robustness	CodeCode Available	0
Finding a human-like classifier	Nov 13, 2019	Adversarial RobustnessContinual Learning	—Unverified	0
MadNet: Using a MAD Optimization for Defending Against Adversarial Attacks	Nov 3, 2019	Adversarial DefenseAdversarial Robustness	—Unverified	0
Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks	Nov 3, 2019	Adversarial Robustness	CodeCode Available	0
Fault Tolerance of Neural Networks in Adversarial Settings	Oct 30, 2019	Adversarial RobustnessFairness	—Unverified	0
Distribution Density, Tails, and Outliers in Machine Learning: Metrics and Applications	Oct 29, 2019	Adversarial RobustnessBIG-bench Machine Learning	—Unverified	0
Certified Adversarial Robustness for Deep Reinforcement Learning	Oct 28, 2019	Adversarial RobustnessCollision Avoidance	—Unverified	0
Label Smoothing and Logit Squeezing: A Replacement for Adversarial Training?	Oct 25, 2019	Adversarial Robustness	—Unverified	0
A Useful Taxonomy for Adversarial Robustness of Neural Networks	Oct 23, 2019	Adversarial Robustness	—Unverified	0
Are Perceptually-Aligned Gradients a General Property of Robust Classifiers?	Oct 18, 2019	Adversarial Robustness	—Unverified	0
An empirical study of pretrained representations for few-shot classification	Oct 3, 2019	Adversarial RobustnessClassification	—Unverified	0
Adversarial Robustness vs. Model Compression, or Both?	Oct 1, 2019	Adversarial Robustnessmodel	CodeCode Available	0
Role of Spatial Context in Adversarial Robustness for Object Detection	Sep 30, 2019	Adversarial AttackAdversarial Robustness	CodeCode Available	0
Impact of Low-bitwidth Quantization on the Adversarial Robustness for Embedded Neural Networks	Sep 27, 2019	Adversarial RobustnessBIG-bench Machine Learning	—Unverified	0
Lower Bounds on Adversarial Robustness from Optimal Transport	Sep 26, 2019	Adversarial RobustnessClassification	CodeCode Available	0
Visual Interpretability Alone Helps Adversarial Robustness	Sep 25, 2019	Adversarial Robustness	—Unverified	0
Global Adversarial Robustness Guarantees for Neural Networks	Sep 25, 2019	Adversarial Robustness	—Unverified	0
_1 Adversarial Robustness Certificates: a Randomized Smoothing Approach	Sep 25, 2019	Adversarial Robustness	—Unverified	0
Towards Disentangling Non-Robust and Robust Components in Performance Metric	Sep 25, 2019	Adversarial RobustnessRelation	—Unverified	0
SPROUT: Self-Progressing Robust Training	Sep 25, 2019	Adversarial Robustness	—Unverified	0
Power up! Robust Graph Convolutional Network based on Graph Powering	Sep 25, 2019	Adversarial Robustness	—Unverified	0
Defending Against Adversarial Examples by Regularized Deep Embedding	Sep 25, 2019	Adversarial AttackAdversarial Robustness	—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