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 1226–1250 of 1746 papers

Title	Date	Tasks	Status
Adversarial Training for Face Recognition Systems using Contrastive Adversarial Learning and Triplet Loss Fine-tuning	Oct 9, 2021	Adversarial RobustnessFace Recognition	—Unverified
Observations on K-image Expansion of Image-Mixing Augmentation for Classification	Oct 8, 2021	Adversarial RobustnessClassification	CodeCode Available
Adversarial Robustness of Program Synthesis Models	Oct 8, 2021	Adversarial RobustnessProgram Synthesis	—Unverified
Improving Adversarial Robustness for Free with Snapshot Ensemble	Oct 7, 2021	Adversarial Robustness	—Unverified
Adversarial Robustness Verification and Attack Synthesis in Stochastic Systems	Oct 5, 2021	Adversarial Robustness	CodeCode Available
Adversarial Robustness via Adaptive Label Smoothing	Sep 29, 2021	Adversarial Robustness	—Unverified
Empirical Study of the Decision Region and Robustness in Deep Neural Networks	Sep 29, 2021	Adversarial AttackAdversarial Robustness	—Unverified
An Empirical Study of Accuracy, Fairness, Explainability, Distributional Robustness, and Adversarial Robustness	Sep 29, 2021	Adversarial RobustnessFairness	—Unverified
Efficient Certification for Probabilistic Robustness	Sep 29, 2021	Adversarial Robustness	—Unverified
Function-Space Variational Inference for Deep Bayesian Classification	Sep 29, 2021	Adversarial RobustnessClassification	—Unverified
GARNET: A Spectral Approach to Robust and Scalable Graph Neural Networks	Sep 29, 2021	Adversarial RobustnessGraph Embedding	—Unverified
Provably Robust Transfer	Sep 29, 2021	Adversarial RobustnessTransfer Learning	—Unverified
Does Adversarial Robustness Really Imply Backdoor Vulnerability?	Sep 29, 2021	Adversarial Robustness	—Unverified
Dissecting Local Properties of Adversarial Examples	Sep 29, 2021	Adversarial Robustness	—Unverified
Delving into Feature Space: Improving Adversarial Robustness by Feature Spectral Regularization	Sep 29, 2021	Adversarial RobustnessAttribute	—Unverified
Use of small auxiliary networks and scarce data to improve the adversarial robustness of deep learning models	Sep 29, 2021	Adversarial Robustnessimage-classification	—Unverified
Resilience to Multiple Attacks via Adversarially Trained MIMO Ensembles	Sep 29, 2021	Adversarial Robustness	—Unverified
Certified Adversarial Robustness Under the Bounded Support Set	Sep 29, 2021	Adversarial Robustness	—Unverified
Learning Sample Reweighting for Adversarial Robustness	Sep 29, 2021	Adversarial RobustnessBilevel Optimization	—Unverified
Towards Achieving Adversarial Robustness Beyond Perceptual Limits	Sep 29, 2021	Adversarial DefenseAdversarial Robustness	—Unverified
k-Mixup Regularization for Deep Learning via Optimal Transport	Sep 29, 2021	Adversarial RobustnessDeep Learning	—Unverified
Biased Multi-Domain Adversarial Training	Sep 29, 2021	Adversarial Robustness	—Unverified
Two Souls in an Adversarial Image: Towards Universal Adversarial Example Detection using Multi-view Inconsistency	Sep 25, 2021	Adversarial Attack DetectionAdversarial Defense	CodeCode Available
CC-Cert: A Probabilistic Approach to Certify General Robustness of Neural Networks	Sep 22, 2021	Adversarial Robustness	CodeCode Available
Robust Physical-World Attacks on Face Recognition	Sep 20, 2021	Adversarial AttackAdversarial 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