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 1551–1575 of 1746 papers

Title	Date	Tasks	Status
SOAR: Second-Order Adversarial Regularization	Apr 4, 2020	Adversarial Robustness	—Unverified
Improving out-of-distribution generalization via multi-task self-supervised pretraining	Mar 30, 2020	Adversarial RobustnessDomain Generalization	—Unverified
Towards Deep Learning Models Resistant to Large Perturbations	Mar 30, 2020	Adversarial RobustnessDeep Learning	CodeCode Available
Challenging the adversarial robustness of DNNs based on error-correcting output codes	Mar 26, 2020	Adversarial AttackAdversarial Robustness	—Unverified
Defense Through Diverse Directions	Mar 24, 2020	Adversarial Robustness	—Unverified
Architectural Resilience to Foreground-and-Background Adversarial Noise	Mar 23, 2020	Adversarial Robustness	CodeCode Available
SAT: Improving Adversarial Training via Curriculum-Based Loss Smoothing	Mar 18, 2020	Adversarial Robustness	—Unverified
Metrics and methods for robustness evaluation of neural networks with generative models	Mar 4, 2020	Adversarial Robustnessimage-classification	CodeCode Available
Understanding the Intrinsic Robustness of Image Distributions using Conditional Generative Models	Mar 1, 2020	Adversarial Robustness	CodeCode Available
Defense-PointNet: Protecting PointNet Against Adversarial Attacks	Feb 27, 2020	Adversarial Robustness	—Unverified
Can we have it all? On the Trade-off between Spatial and Adversarial Robustness of Neural Networks	Feb 26, 2020	Adversarial RobustnessAll	CodeCode Available
Towards Certifiable Adversarial Sample Detection	Feb 20, 2020	Adversarial Robustness	—Unverified
Individual Fairness Revisited: Transferring Techniques from Adversarial Robustness	Feb 18, 2020	Adversarial RobustnessFairness	—Unverified
Scalable Quantitative Verification For Deep Neural Networks	Feb 17, 2020	Adversarial Robustness	—Unverified
CEB Improves Model Robustness	Feb 13, 2020	Adversarial RobustnessData Augmentation	CodeCode Available
Semialgebraic Optimization for Lipschitz Constants of ReLU Networks	Feb 10, 2020	Adversarial Robustness	CodeCode Available
Assessing the Adversarial Robustness of Monte Carlo and Distillation Methods for Deep Bayesian Neural Network Classification	Feb 7, 2020	Adversarial RobustnessGeneral Classification	—Unverified
Guess First to Enable Better Compression and Adversarial Robustness	Jan 10, 2020	Adversarial Robustness	—Unverified
RECAST: Interactive Auditing of Automatic Toxicity Detection Models	Jan 7, 2020	Adversarial RobustnessFairness	—Unverified
Optimal Statistical Guaratees for Adversarially Robust Gaussian Classification	Jan 1, 2020	Adversarial RobustnessClassification	—Unverified
Adversarially Robust Neural Networks via Optimal Control: Bridging Robustness with Lyapunov Stability	Jan 1, 2020	Adversarial Robustness	—Unverified
Smooth Kernels Improve Adversarial Robustness and Perceptually-Aligned Gradients	Jan 1, 2020	Adversarial Robustness	—Unverified
Adversarial Robustness via Runtime Masking and Cleansing	Jan 1, 2020	Adversarial Robustness	—Unverified
Implicit Euler Skip Connections: Enhancing Adversarial Robustness via Numerical Stability	Jan 1, 2020	Adversarial AttackAdversarial Robustness	—Unverified
Optimising Neural Network Architectures for Provable Adversarial Robustness	Jan 1, 2020	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