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
Benchmarking Adversarial Robustness	Dec 26, 2019	Adversarial AttackAdversarial Robustness	—Unverified
P-CapsNets: a General Form of Convolutional Neural Networks	Dec 18, 2019	Adversarial RobustnessForm	—Unverified
APRICOT: A Dataset of Physical Adversarial Attacks on Object Detection	Dec 17, 2019	Adversarial RobustnessDensity Estimation	CodeCode Available
What it Thinks is Important is Important: Robustness Transfers through Input Gradients	Dec 11, 2019	Adversarial Robustness	CodeCode Available
Feature Losses for Adversarial Robustness	Dec 10, 2019	Adversarial RobustnessDenoising	—Unverified
Exploring the Back Alleys: Analysing The Robustness of Alternative Neural Network Architectures against Adversarial Attacks	Dec 8, 2019	Adversarial Robustness	—Unverified
An Empirical Study on the Relation between Network Interpretability and Adversarial Robustness	Dec 7, 2019	Adversarial RobustnessImage Classification	CodeCode Available
Towards Robust Image Classification Using Sequential Attention Models	Dec 4, 2019	Adversarial RobustnessClassification	—Unverified
Error Correcting Output Codes Improve Probability Estimation and Adversarial Robustness of Deep Neural Networks	Dec 1, 2019	Adversarial DefenseAdversarial Robustness	CodeCode Available
Can Attention Masks Improve Adversarial Robustness?	Nov 27, 2019	Adversarial Robustness	—Unverified
An Adaptive View of Adversarial Robustness from Test-time Smoothing Defense	Nov 26, 2019	Adversarial RobustnessDecision Making	CodeCode Available
Playing it Safe: Adversarial Robustness with an Abstain Option	Nov 25, 2019	Adversarial Robustness	—Unverified
CAMUS: A Framework to Build Formal Specifications for Deep Perception Systems Using Simulators	Nov 25, 2019	Adversarial Robustness	—Unverified
Verifiability and Predictability: Interpreting Utilities of Network Architectures for Point Cloud Processing	Nov 20, 2019	Adversarial Robustness	CodeCode Available
Adversarial Robustness of Flow-Based Generative Models	Nov 20, 2019	Adversarial Robustness	—Unverified
AdvKnn: Adversarial Attacks On K-Nearest Neighbor Classifiers With Approximate Gradients	Nov 15, 2019	Adversarial Robustness	CodeCode Available
Finding a human-like classifier	Nov 13, 2019	Adversarial RobustnessContinual Learning	—Unverified
Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks	Nov 3, 2019	Adversarial Robustness	CodeCode Available
MadNet: Using a MAD Optimization for Defending Against Adversarial Attacks	Nov 3, 2019	Adversarial DefenseAdversarial Robustness	—Unverified
Fault Tolerance of Neural Networks in Adversarial Settings	Oct 30, 2019	Adversarial RobustnessFairness	—Unverified
Distribution Density, Tails, and Outliers in Machine Learning: Metrics and Applications	Oct 29, 2019	Adversarial RobustnessBIG-bench Machine Learning	—Unverified
Certified Adversarial Robustness for Deep Reinforcement Learning	Oct 28, 2019	Adversarial RobustnessCollision Avoidance	—Unverified
Label Smoothing and Logit Squeezing: A Replacement for Adversarial Training?	Oct 25, 2019	Adversarial Robustness	—Unverified
A Useful Taxonomy for Adversarial Robustness of Neural Networks	Oct 23, 2019	Adversarial Robustness	—Unverified
Are Perceptually-Aligned Gradients a General Property of Robust Classifiers?	Oct 18, 2019	Adversarial Robustness	—Unverified

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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