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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 1261–1270 of 1746 papers

Title	Date	Tasks	Status	Hype
Improved Adversarial Robustness by Reducing Open Space Risk via Tent Activations	Aug 7, 2019	Adversarial RobustnessBIG-bench Machine Learning	—Unverified	0
Improved Adversarial Robustness via Logit Regularization Methods	Jun 10, 2019	Adversarial DefenseAdversarial Robustness	—Unverified	0
Improved Branch and Bound for Neural Network Verification via Lagrangian Decomposition	Apr 14, 2021	Adversarial RobustnessGPU	—Unverified	0
MadNet: Using a MAD Optimization for Defending Against Adversarial Attacks	Nov 3, 2019	Adversarial DefenseAdversarial Robustness	—Unverified	0
Improve Generalization and Robustness of Neural Networks via Weight Scale Shifting Invariant Regularizations	Aug 7, 2020	Adversarial Robustness	—Unverified	0
Improving Adversarial Robustness by Encouraging Discriminative Features	Nov 1, 2018	Adversarial Robustness	—Unverified	0
Improving Adversarial Robustness by Contrastive Guided Diffusion Process	Oct 18, 2022	Adversarial RobustnessSynthetic Data Generation	—Unverified	0
Improving Adversarial Robustness for 3D Point Cloud Recognition at Test-Time through Purified Self-Training	Sep 23, 2024	Adversarial PurificationAdversarial Robustness	—Unverified	0
Improving Adversarial Robustness for Free with Snapshot Ensemble	Oct 7, 2021	Adversarial Robustness	—Unverified	0
Improving Adversarial Robustness in Weight-quantized Neural Networks	Dec 29, 2020	Adversarial RobustnessQuantization	—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