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 351–375 of 1746 papers

Title	Date	Tasks	Status	Score
advertorch v0.1: An Adversarial Robustness Toolbox based on PyTorch	Feb 20, 2019	Adversarial AttackAdversarial Defense	CodeCode Available	5
Finding Biological Plausibility for Adversarially Robust Features via Metameric Tasks	Feb 2, 2022	Adversarial RobustnessTexture Synthesis	CodeCode Available	5
FI-ODE: Certifiably Robust Forward Invariance in Neural ODEs	Oct 30, 2022	Adversarial Robustnesscontinuous-control	CodeCode Available	5
Generating Adversarial Samples in Mini-Batches May Be Detrimental To Adversarial Robustness	Mar 30, 2023	Adversarial Robustness	CodeCode Available	5
Adversaries With Incentives: A Strategic Alternative to Adversarial Robustness	Jun 17, 2024	Adversarial RobustnessInductive Bias	CodeCode Available	5
Adversarial Machine Learning in Latent Representations of Neural Networks	Sep 29, 2023	Adversarial RobustnessEdge-computing	CodeCode Available	5
Model Compression with Adversarial Robustness: A Unified Optimization Framework	Feb 10, 2019	Adversarial RobustnessModel Compression	CodeCode Available	5
Feature Statistics with Uncertainty Help Adversarial Robustness	Mar 26, 2025	Adversarial Robustness	CodeCode Available	5
Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack	Jun 15, 2022	Adversarial RobustnessComputational Efficiency	CodeCode Available	5
Adversarial Training and Robustness for Multiple Perturbations	Apr 30, 2019	Adversarial Robustness	CodeCode Available	5
Fast Adversarial Training with Smooth Convergence	Aug 24, 2023	Adversarial Robustness	CodeCode Available	5
Fast Adversarial Robustness Certification of Nearest Prototype Classifiers for Arbitrary Seminorms	Dec 1, 2020	Adversarial RobustnessQuantization	CodeCode Available	5
FairDeFace: Evaluating the Fairness and Adversarial Robustness of Face Obfuscation Methods	Mar 11, 2025	Adversarial RobustnessFace Detection	CodeCode Available	5
Adversarial Robustness with Non-uniform Perturbations	Feb 24, 2021	Adversarial RobustnessMalware Classification	CodeCode Available	5
FaiR-N: Fair and Robust Neural Networks for Structured Data	Oct 13, 2020	Adversarial RobustnessAttribute	CodeCode Available	5
Adversarially Robust Spiking Neural Networks Through Conversion	Nov 15, 2023	Adversarial Robustness	CodeCode Available	5
Adversarial Robustness vs. Model Compression, or Both?	Oct 1, 2019	Adversarial Robustnessmodel	CodeCode Available	5
Expressivity of Graph Neural Networks Through the Lens of Adversarial Robustness	Aug 16, 2023	Adversarial RobustnessSubgraph Counting	CodeCode Available	5
Fake It Until You Break It: On the Adversarial Robustness of AI-generated Image Detectors	Oct 2, 2024	Adversarial RobustnessMisinformation	CodeCode Available	5
Feature Denoising for Improving Adversarial Robustness	Dec 9, 2018	Adversarial DefenseAdversarial Robustness	CodeCode Available	5
Exploring Adversarial Robustness of Deep Metric Learning	Feb 14, 2021	Adversarial RobustnessMetric Learning	CodeCode Available	5
Adversarial robustness via robust low rank representations	Jul 13, 2020	Adversarial Robustness	CodeCode Available	5
Adversarially Robust One-class Novelty Detection	Aug 25, 2021	Adversarial RobustnessNovelty Detection	CodeCode Available	5
Adversarial Robustness via Fisher-Rao Regularization	Jun 12, 2021	Adversarial DefenseAdversarial Robustness	CodeCode Available	5
Exploring Adversarial Attacks and Defenses in Vision Transformers trained with DINO	Jun 14, 2022	Adversarial Robustness	CodeCode Available	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