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 351–375 of 1746 papers

Title	Date	Tasks	Status
A Frequency Perspective of Adversarial Robustness	Oct 26, 2021	Adversarial Robustness	—Unverified
Adversarial Prompt Distillation for Vision-Language Models	Nov 22, 2024	Adversarial RobustnessAutonomous Driving	—Unverified
A Framework for Verification of Wasserstein Adversarial Robustness	Oct 13, 2021	Adversarial AttackAdversarial Robustness	—Unverified
A Flat Minima Perspective on Understanding Augmentations and Model Robustness	May 30, 2025	Adversarial RobustnessData Augmentation	—Unverified
AdvFunMatch: When Consistent Teaching Meets Adversarial Robustness	May 24, 2023	Adversarial RobustnessKnowledge Distillation	—Unverified
A Finer Calibration Analysis for Adversarial Robustness	May 4, 2021	Adversarial RobustnessBIG-bench Machine Learning	—Unverified
Affine-Invariant Robust Training	Oct 8, 2020	Adversarial RobustnessBIG-bench Machine Learning	—Unverified
Adversarial Amendment is the Only Force Capable of Transforming an Enemy into a Friend	May 18, 2023	Adversarial AttackAdversarial Defense	—Unverified
Deadwooding: Robust Global Pruning for Deep Neural Networks	Feb 10, 2022	Adversarial Robustness	—Unverified
Adversarial Masked Autoencoder Purifier with Defense Transferability	Jan 28, 2025	Adversarial DefenseAdversarial Robustness	—Unverified
DataFreeShield: Defending Adversarial Attacks without Training Data	Jun 21, 2024	Adversarial RobustnessDataset Generation	—Unverified
AdvFilter: Predictive Perturbation-aware Filtering against Adversarial Attack via Multi-domain Learning	Jul 14, 2021	Adversarial AttackAdversarial Robustness	—Unverified
Adversarial Alignment for LLMs Requires Simpler, Reproducible, and More Measurable Objectives	Feb 17, 2025	Adversarial Robustness	—Unverified
Mining Data Impressions from Deep Models as Substitute for the Unavailable Training Data	Jan 15, 2021	Adversarial RobustnessContinual Learning	—Unverified
Deciphering the Definition of Adversarial Robustness for post-hoc OOD Detectors	Jun 21, 2024	Adversarial DefenseAdversarial Robustness	—Unverified
DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models	Jun 20, 2023	Adversarial RobustnessEthics	—Unverified
Adversary Agnostic Robust Deep Reinforcement Learning	Aug 14, 2020	Adversarial RobustnessAtari Games	—Unverified
Cross-Entropy Loss Functions: Theoretical Analysis and Applications	Apr 14, 2023	Adversarial Robustness	—Unverified
CSTAR: Towards Compact and STructured Deep Neural Networks with Adversarial Robustness	Dec 4, 2022	Adversarial RobustnessModel Compression	—Unverified
Adversarially Robust Video Perception by Seeing Motion	Dec 13, 2022	Adversarial Robustness	—Unverified
Adversarial Training via Adaptive Knowledge Amalgamation of an Ensemble of Teachers	May 22, 2024	Adversarial Robustness	—Unverified
Adversarial alignment: Breaking the trade-off between the strength of an attack and its relevance to human perception	Jun 5, 2023	Adversarial AttackAdversarial Robustness	—Unverified
Curvature Dynamic Black-box Attack: revisiting adversarial robustness via dynamic curvature estimation	May 25, 2025	Adversarial AttackAdversarial Robustness	—Unverified
Adversarial Training for Face Recognition Systems using Contrastive Adversarial Learning and Triplet Loss Fine-tuning	Oct 9, 2021	Adversarial RobustnessFace Recognition	—Unverified
Adversarially-Robust TD Learning with Markovian Data: Finite-Time Rates and Fundamental Limits	Feb 7, 2025	Adversarial RobustnessReinforcement Learning (RL)	—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