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 1476–1500 of 1746 papers

Title	Date	Tasks	Status
Queried Unlabeled Data Improves and Robustifies Class-Incremental Learning	Jun 15, 2022	Adversarial Robustnessclass-incremental learning	CodeCode Available
Improving Adversarial Robustness via Probabilistically Compact Loss with Logit Constraints	Dec 14, 2020	Adversarial Robustness	CodeCode Available
Improving Adversarial Robustness via Decoupled Visual Representation Masking	Jun 16, 2024	Adversarial DefenseAdversarial Robustness	CodeCode Available
Level Up with ML Vulnerability Identification: Leveraging Domain Constraints in Feature Space for Robust Android Malware Detection	May 30, 2022	Adversarial RobustnessAndroid Malware Detection	CodeCode Available
Towards Understanding Adversarial Robustness of Optical Flow Networks	Mar 30, 2021	Adversarial RobustnessOptical Flow Estimation	CodeCode Available
Improving Adversarial Robustness via Guided Complement Entropy	Mar 23, 2019	Adversarial DefenseAdversarial Robustness	CodeCode Available
Improving Adversarial Robustness via Joint Classification and Multiple Explicit Detection Classes	Oct 26, 2022	Adversarial RobustnessRobust classification	CodeCode Available
Does language help generalization in vision models?	Apr 16, 2021	Adversarial RobustnessClustering	CodeCode Available
RAID: A Dataset for Testing the Adversarial Robustness of AI-Generated Image Detectors	Jun 4, 2025	Adversarial Robustness	CodeCode Available
BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation	Nov 14, 2024	Adversarial AttackAdversarial Robustness	CodeCode Available
Improving Adversarial Robustness with Self-Paced Hard-Class Pair Reweighting	Oct 26, 2022	Adversarial RobustnessModel Optimization	CodeCode Available
Bayesian Inference with Certifiable Adversarial Robustness	Feb 10, 2021	Adversarial RobustnessBayesian Inference	CodeCode Available
An Empirical Study on the Relation between Network Interpretability and Adversarial Robustness	Dec 7, 2019	Adversarial RobustnessImage Classification	CodeCode Available
Improving Robustness by Enhancing Weak Subnets	Jan 30, 2022	Adversarial RobustnessData Augmentation	CodeCode Available
Adversarial Ensemble Training by Jointly Learning Label Dependencies and Member Models	Jun 29, 2022	Adversarial RobustnessDiversity	CodeCode Available
Improving Document Binarization via Adversarial Noise-Texture Augmentation	Oct 25, 2018	Adversarial RobustnessBinarization	CodeCode Available
Improving Ensemble Robustness by Collaboratively Promoting and Demoting Adversarial Robustness	Sep 21, 2020	Adversarial RobustnessDiversity	CodeCode Available
RAMP: Boosting Adversarial Robustness Against Multiple l_p Perturbations for Universal Robustness	Feb 9, 2024	Adversarial Robustness	CodeCode Available
Disentangling Adversarial Robustness in Directions of the Data Manifold	Jan 1, 2021	Adversarial Robustness	CodeCode Available
Adversarial Robustness through the Lens of Convolutional Filters	Apr 5, 2022	Adversarial Robustness	CodeCode Available
Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks	Jan 5, 2023	Adversarial Robustness	CodeCode Available
Randomized Smoothing with Masked Inference for Adversarially Robust Text Classifications	May 11, 2023	Adversarial Robustness	CodeCode Available
Adversarial Robustness Study of Convolutional Neural Network for Lumbar Disk Shape Reconstruction from MR images	Feb 4, 2021	Adversarial RobustnessData Augmentation	CodeCode Available
Adversarial Concurrent Training: Optimizing Robustness and Accuracy Trade-off of Deep Neural Networks	Aug 16, 2020	Adversarial Robustness	CodeCode Available
Batch Normalization Increases Adversarial Vulnerability and Decreases Adversarial Transferability: A Non-Robust Feature Perspective	Oct 7, 2020	Adversarial Robustness	CodeCode Available

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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