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 1501–1525 of 1746 papers

Title	Date	Tasks	Status
A Comprehensive Study on Robustness of Image Classification Models: Benchmarking and Rethinking	Feb 28, 2023	Adversarial RobustnessBenchmarking	—Unverified
Developing Assurance Cases for Adversarial Robustness and Regulatory Compliance in LLMs	Oct 4, 2024	Adversarial RobustnessManagement	—Unverified
Canonical Latent Representations in Conditional Diffusion Models	Jun 11, 2025	Adversarial RobustnessRepresentation Learning	—Unverified
Differentially Private Adversarial Robustness Through Randomized Perturbations	Sep 27, 2020	Adversarial RobustnessSemantic Similarity	—Unverified
Differentially Private Optimizers Can Learn Adversarially Robust Models	Nov 16, 2022	Adversarial Robustness	—Unverified
A3T: Adversarially Augmented Adversarial Training	Jan 12, 2018	Adversarial RobustnessBIG-bench Machine Learning	—Unverified
Adversarial Robustness of Link Sign Prediction in Signed Graphs	Jan 19, 2024	Adversarial RobustnessContrastive Learning	—Unverified
DiffuseMix: Label-Preserving Data Augmentation with Diffusion Models	Apr 5, 2024	Adversarial RobustnessData Augmentation	—Unverified
Scoring Black-Box Models for Adversarial Robustness	Oct 31, 2022	Adversarial Robustness	—Unverified
Two Heads are Better than One: Towards Better Adversarial Robustness by Combining Transduction and Rejection	May 27, 2023	Adversarial Robustness	—Unverified
Certified Robustness to Clean-Label Poisoning Using Diffusion Denoising	Mar 18, 2024	Adversarial RobustnessDenoising	—Unverified
DiPSeN: Differentially Private Self-normalizing Neural Networks For Adversarial Robustness in Federated Learning	Jan 8, 2021	Adversarial RobustnessBIG-bench Machine Learning	—Unverified
Discretization based Solutions for Secure Machine Learning against Adversarial Attacks	Feb 8, 2019	Adversarial RobustnessBIG-bench Machine Learning	—Unverified
Disentangled Text Representation Learning with Information-Theoretic Perspective for Adversarial Robustness	Oct 26, 2022	Adversarial RobustnessRepresentation Learning	—Unverified
Second Order Optimization for Adversarial Robustness and Interpretability	Sep 10, 2020	Adversarial Robustness	—Unverified
Two is Better than One: Efficient Ensemble Defense for Robust and Compact Models	Apr 7, 2025	Adversarial RobustnessDiversity	—Unverified
Dissecting Local Properties of Adversarial Examples	Sep 29, 2021	Adversarial Robustness	—Unverified
Can Language Models be Instructed to Protect Personal Information?	Oct 3, 2023	Adversarial RobustnessRed Teaming	—Unverified
Distance-Restricted Explanations: Theoretical Underpinnings & Efficient Implementation	May 14, 2024	Adversarial RobustnessExplainable artificial intelligence	—Unverified
Distilled Agent DQN for Provable Adversarial Robustness	Sep 27, 2018	Adversarial Robustnessreinforcement-learning	—Unverified
Distilling Adversarial Robustness Using Heterogeneous Teachers	Feb 23, 2024	Adversarial RobustnessKnowledge Distillation	—Unverified
Can Implicit Bias Imply Adversarial Robustness?	May 24, 2024	Adversarial Robustness	—Unverified
Does Adversarial Robustness Really Imply Backdoor Vulnerability?	Sep 29, 2021	Adversarial Robustness	—Unverified
SecPE: Secure Prompt Ensembling for Private and Robust Large Language Models	Feb 2, 2025	Adversarial RobustnessPrivacy Preserving	—Unverified
Secure Diagnostics: Adversarial Robustness Meets Clinical Interpretability	Apr 7, 2025	Adversarial AttackAdversarial Robustness	—Unverified

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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