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 251–275 of 1746 papers

Title	Date	Tasks	Status	Hype	Score
Adversarial Robustness against Multiple and Single l_p-Threat Models via Quick Fine-Tuning of Robust Classifiers	May 26, 2021	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 Robustness Against the Union of Multiple Perturbation Models	Sep 9, 2019	Adversarial Robustness	CodeCode Available	1	5
SHIELD: Defending Textual Neural Networks against Multiple Black-Box Adversarial Attacks with Stochastic Multi-Expert Patcher	Nov 17, 2020	Adversarial Robustness	CodeCode Available	1	5
Enhancing adversarial robustness in Natural Language Inference using explanations	Sep 11, 2024	Adversarial RobustnessNatural Language Inference	CodeCode Available	1	5
Fast and Low-Cost Genomic Foundation Models via Outlier Removal	May 1, 2025	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
Adversarial Attacks on Graph Classifiers via Bayesian Optimisation	Dec 1, 2021	Adversarial RobustnessBayesian Optimisation	CodeCode Available	1	5
Efficient Exact Verification of Binarized Neural Networks	May 7, 2020	Adversarial Robustness	CodeCode Available	1	5
Adversarial Robustness as a Prior for Learned Representations	Jun 3, 2019	Adversarial Robustness	CodeCode Available	1	5
GARNET: Reduced-Rank Topology Learning for Robust and Scalable Graph Neural Networks	Jan 30, 2022	Adversarial Robustness	CodeCode Available	1	5
A Perturbation-Constrained Adversarial Attack for Evaluating the Robustness of Optical Flow	Mar 24, 2022	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
Eliminating Catastrophic Overfitting Via Abnormal Adversarial Examples Regularization	Apr 11, 2024	Adversarial Robustness	CodeCode Available	1	5
ARAE: Adversarially Robust Training of Autoencoders Improves Novelty Detection	Mar 12, 2020	Adversarial RobustnessNovelty Detection	CodeCode Available	1	5
Enhancing Adversarial Robustness via Score-Based Optimization	Jul 10, 2023	Adversarial DefenseAdversarial Robustness	CodeCode Available	1	5
Enhancing Intrinsic Adversarial Robustness via Feature Pyramid Decoder	May 6, 2020	Adversarial RobustnessDecoder	CodeCode Available	1	5
Ensemble everything everywhere: Multi-scale aggregation for adversarial robustness	Aug 8, 2024	Adversarial AttackAdversarial Robustness	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
A Regularization Method to Improve Adversarial Robustness of Neural Networks for ECG Signal Classification	Oct 19, 2021	Adversarial RobustnessClassification	CodeCode Available	1	5
Adversarial Attacks on ML Defense Models Competition	Oct 15, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
Explainability-Aware One Point Attack for Point Cloud Neural Networks	Oct 8, 2021	3D Object RecognitionAdversarial Robustness	CodeCode Available	1	5
ImageNet-Patch: A Dataset for Benchmarking Machine Learning Robustness against Adversarial Patches	Mar 7, 2022	Adversarial RobustnessBenchmarking	CodeCode Available	1	5
On Evaluating Adversarial Robustness of Volumetric Medical Segmentation Models	Jun 12, 2024	Adversarial RobustnessMamba	CodeCode Available	1	5
Adversarial Robustness for Code	Feb 11, 2020	Adversarial RobustnessBIG-bench Machine Learning	CodeCode Available	1	5
Are Transformers More Robust Than CNNs?	Nov 10, 2021	Adversarial Robustness	CodeCode Available	1	5
Stochastic Local Winner-Takes-All Networks Enable Profound Adversarial Robustness	Dec 5, 2021	Adversarial AttackAdversarial Defense	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