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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 661–670 of 1746 papers

Title	Date	Tasks	Status	Hype	Score
Generating Adversarial Examples with Adversarial Networks	Jan 8, 2018	Adversarial Robustness	CodeCode Available	0	5
Understanding the Robustness of Graph Neural Networks against Adversarial Attacks	Jun 20, 2024	Adversarial Robustness	CodeCode Available	0	5
Generating Adversarial Samples in Mini-Batches May Be Detrimental To Adversarial Robustness	Mar 30, 2023	Adversarial Robustness	CodeCode Available	0	5
The interplay of robustness and generalization in quantum machine learning	Jun 10, 2025	Adversarial RobustnessQuantum Machine Learning	CodeCode Available	0	5
MIMIR: Masked Image Modeling for Mutual Information-based Adversarial Robustness	Dec 8, 2023	Adversarial Robustness	CodeCode Available	0	5
Diffusion Denoised Smoothing for Certified and Adversarial Robust Out-Of-Distribution Detection	Mar 27, 2023	Adversarial RobustnessOut-of-Distribution Detection	CodeCode Available	0	5
MixedNUTS: Training-Free Accuracy-Robustness Balance via Nonlinearly Mixed Classifiers	Feb 3, 2024	Adversarial RobustnessRobust classification	CodeCode Available	0	5
Mixup Inference: Better Exploiting Mixup to Defend Adversarial Attacks	Sep 25, 2019	Adversarial Robustness	CodeCode Available	0	5
Generative Max-Mahalanobis Classifiers for Image Classification, Generation and More	Jan 1, 2021	Adversarial RobustnessClassification	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