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 1651–1675 of 1746 papers

Title	Date	Tasks	Status
Exposing Fine-Grained Adversarial Vulnerability of Face Anti-Spoofing Models	May 30, 2022	Adversarial AttackAdversarial Robustness	—Unverified
Exposing the Robustness and Vulnerability of Hybrid 8T-6T SRAM Memory Architectures to Adversarial Attacks in Deep Neural Networks	Nov 26, 2020	Adversarial Robustness	—Unverified
AdvCat: Domain-Agnostic Robustness Assessment for Cybersecurity-Critical Applications with Categorical Inputs	Dec 13, 2022	Adversarial RobustnessFake News Detection	—Unverified
CARES: Comprehensive Evaluation of Safety and Adversarial Robustness in Medical LLMs	May 16, 2025	Adversarial RobustnessSafety Alignment	—Unverified
Extreme Miscalibration and the Illusion of Adversarial Robustness	Feb 27, 2024	Adversarial AttackAdversarial Robustness	—Unverified
F^2AT: Feature-Focusing Adversarial Training via Disentanglement of Natural and Perturbed Patterns	Oct 23, 2023	Adversarial RobustnessDisentanglement	—Unverified
Facial Attributes: Accuracy and Adversarial Robustness	Jan 4, 2018	Adversarial RobustnessAttribute	—Unverified
FADE: Enabling Federated Adversarial Training on Heterogeneous Resource-Constrained Edge Devices	Sep 8, 2022	Adversarial RobustnessFederated Learning	—Unverified
FADER: Fast Adversarial Example Rejection	Oct 18, 2020	Adversarial Robustness	—Unverified
Failure Modes of Variational Autoencoders and Their Effects on Downstream Tasks	Jul 14, 2020	Adversarial Robustness	—Unverified
Understanding Adversarial Robustness: The Trade-off between Minimum and Average Margin	Jul 26, 2019	Adversarial Robustness	—Unverified
Sparse DNNs with Improved Adversarial Robustness	Oct 23, 2018	Adversarial RobustnessGeneral Classification	—Unverified
Fair Robust Active Learning by Joint Inconsistency	Sep 22, 2022	Active LearningAdversarial Attack	—Unverified
FAIR-TAT: Improving Model Fairness Using Targeted Adversarial Training	Oct 30, 2024	Adversarial RobustnessFairness	—Unverified
Faithful Knowledge Distillation	Jun 7, 2023	Adversarial RobustnessKnowledge Distillation	—Unverified
Spatial-Temporal-Fusion BNN: Variational Bayesian Feature Layer	Dec 12, 2021	Adversarial RobustnessUncertainty Quantification	—Unverified
Understanding Adversarial Robustness Through Loss Landscape Geometries	Jul 22, 2019	Adversarial RobustnessData Augmentation	—Unverified
Fast Adversarial Training against Textual Adversarial Attacks	Jan 23, 2024	Adversarial DefenseAdversarial Robustness	—Unverified
Understanding and Measuring Robustness of Multimodal Learning	Dec 22, 2021	Adversarial Robustness	—Unverified
Fast Adversarial Training with Weak-to-Strong Spatial-Temporal Consistency in the Frequency Domain on Videos	Apr 21, 2025	Adversarial RobustnessVideo Recognition	—Unverified
Beyond cross-entropy: learning highly separable feature distributions for robust and accurate classification	Oct 29, 2020	Adversarial RobustnessGeneral Classification	—Unverified
SpeechGuard: Exploring the Adversarial Robustness of Multimodal Large Language Models	May 14, 2024	Adversarial RobustnessInstruction Following	—Unverified
Beyond Classification: Evaluating Diffusion Denoised Smoothing for Security-Utility Trade off	May 21, 2025	Adversarial AttackAdversarial Robustness	—Unverified
Better Representations via Adversarial Training in Pre-Training: A Theoretical Perspective	Jan 26, 2024	Adversarial RobustnessContrastive Learning	—Unverified
FAT: Federated Adversarial Training	Dec 3, 2020	Adversarial RobustnessFederated Learning	—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