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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 391–400 of 1746 papers

Title	Date	Tasks	Status	Hype	Score
Boosting Adversarial Robustness using Feature Level Stochastic Smoothing	Jun 10, 2023	Adversarial RobustnessAutonomous Navigation	CodeCode Available	0	5
Adversarially Robust Learning with Optimal Transport Regularized Divergences	Sep 7, 2023	Adversarial RobustnessDeep Learning	CodeCode Available	0	5
Fake It Until You Break It: On the Adversarial Robustness of AI-generated Image Detectors	Oct 2, 2024	Adversarial RobustnessMisinformation	CodeCode Available	0	5
Fast Adversarial Training with Smooth Convergence	Aug 24, 2023	Adversarial Robustness	CodeCode Available	0	5
Expressivity of Graph Neural Networks Through the Lens of Adversarial Robustness	Aug 16, 2023	Adversarial RobustnessSubgraph Counting	CodeCode Available	0	5
Expressive Losses for Verified Robustness via Convex Combinations	May 23, 2023	Adversarial Robustness	CodeCode Available	0	5
FairDeFace: Evaluating the Fairness and Adversarial Robustness of Face Obfuscation Methods	Mar 11, 2025	Adversarial RobustnessFace Detection	CodeCode Available	0	5
Bridging the Gap Between Adversarial Robustness and Optimization Bias	Feb 17, 2021	Adversarial Robustness	CodeCode Available	0	5
BNN-DP: Robustness Certification of Bayesian Neural Networks via Dynamic Programming	Jun 19, 2023	Adversarial RobustnessComputational Efficiency	CodeCode Available	0	5
FaiR-N: Fair and Robust Neural Networks for Structured Data	Oct 13, 2020	Adversarial RobustnessAttribute	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