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 51–75 of 1746 papers

Title	Date	Tasks	Status	Hype	Score
Adversarial Robustness for Code	Feb 11, 2020	Adversarial RobustnessBIG-bench Machine Learning	CodeCode Available	1	5
Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNs	Oct 6, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
Adversarial Machine Learning: Bayesian Perspectives	Mar 7, 2020	Adversarial RobustnessBIG-bench Machine Learning	CodeCode Available	1	5
Achieving robustness in classification using optimal transport with hinge regularization	Jun 11, 2020	Adversarial RobustnessBinary Classification	CodeCode Available	1	5
GenoArmory: A Unified Evaluation Framework for Adversarial Attacks on Genomic Foundation Models	May 16, 2025	Adversarial AttackAdversarial Defense	CodeCode Available	1	5
Adversarial Robustness in Graph Neural Networks: A Hamiltonian Approach	Oct 10, 2023	Adversarial Robustness	CodeCode Available	1	5
Adversarial Vulnerability of Randomized Ensembles	Jun 14, 2022	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
Adversarial Attack and Defense Strategies for Deep Speaker Recognition Systems	Aug 18, 2020	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
Adversarial Attack on Deep Learning-Based Splice Localization	Apr 17, 2020	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
Adversarial Robustness of Bottleneck Injected Deep Neural Networks for Task-Oriented Communication	Dec 13, 2024	Adversarial Robustness	CodeCode Available	1	5
Adversarial Robustness Limits via Scaling-Law and Human-Alignment Studies	Apr 14, 2024	Adversarial Robustness	CodeCode Available	1	5
Adversarial Attacks on Graph Classification via Bayesian Optimisation	Nov 4, 2021	Adversarial RobustnessBayesian Optimisation	CodeCode Available	1	5
Adversarial Vertex Mixup: Toward Better Adversarially Robust Generalization	Mar 5, 2020	Adversarial RobustnessData Augmentation	CodeCode Available	1	5
AdvRush: Searching for Adversarially Robust Neural Architectures	Aug 3, 2021	Adversarial RobustnessNeural Architecture Search	CodeCode Available	1	5
A Light Recipe to Train Robust Vision Transformers	Sep 15, 2022	Adversarial RobustnessData Augmentation	CodeCode Available	1	5
Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Models	Nov 4, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
Adversarially-Aware Robust Object Detector	Jul 13, 2022	Adversarial RobustnessObject	CodeCode Available	1	5
Adversarial Robustness under Long-Tailed Distribution	Apr 6, 2021	Adversarial Robustness	CodeCode Available	1	5
CausalAdv: Adversarial Robustness through the Lens of Causality	Jun 11, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
Adversarial Robustness via Random Projection Filters	Jan 1, 2023	Adversarial RobustnessAttribute	CodeCode Available	1	5
Adversarial Contrastive Learning via Asymmetric InfoNCE	Jul 18, 2022	Adversarial RobustnessContrastive Learning	CodeCode Available	1	5
Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks	Nov 1, 2022	Adversarial RobustnessPseudo Label	CodeCode Available	1	5
Adversarial Visual Robustness by Causal Intervention	Jun 17, 2021	Adversarial Robustness	CodeCode Available	1	5
Adversarial vulnerability of powerful near out-of-distribution detection	Jan 18, 2022	Adversarial RobustnessOut-of-Distribution Detection	CodeCode Available	1	5
Adversarial Attacks on ML Defense Models Competition	Oct 15, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1	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