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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 731–740 of 1746 papers

Title	Date	Tasks	Status	Hype	Score
Hardening DNNs against Transfer Attacks during Network Compression using Greedy Adversarial Pruning	Jun 15, 2022	Adversarial RobustnessQuantization	CodeCode Available	0	5
Give me a hint: Can LLMs take a hint to solve math problems?	Oct 8, 2024	Adversarial RobustnessMath	CodeCode Available	0	5
On Adversarial Robustness: A Neural Architecture Search perspective	Jul 16, 2020	Adversarial RobustnessNeural Architecture Search	CodeCode Available	0	5
RDI: An adversarial robustness evaluation metric for deep neural networks based on model statistical features	Apr 16, 2025	Adversarial RobustnessComputational Efficiency	CodeCode Available	0	5
Global-Local Regularization Via Distributional Robustness	Mar 1, 2022	Adversarial RobustnessDomain Adaptation	CodeCode Available	0	5
Testing Robustness Against Unforeseen Adversaries	Aug 21, 2019	Adversarial DefenseAdversarial Robustness	CodeCode Available	0	5
Adversarial Robustness by Design through Analog Computing and Synthetic Gradients	Jan 6, 2021	Adversarial RobustnessBinarization	CodeCode Available	0	5
Get Fooled for the Right Reason: Improving Adversarial Robustness through a Teacher-guided Curriculum Learning Approach	Oct 30, 2021	Adversarial Robustness	CodeCode Available	0	5
Adversarial Robustness of VAEs across Intersectional Subgroups	Jul 4, 2024	Adversarial RobustnessDimensionality Reduction	CodeCode Available	0	5
Gradient-Free Adversarial Attacks for Bayesian Neural Networks	Dec 23, 2020	Adversarial RobustnessBayesian Inference	CodeCode Available	0	5

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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