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 976–1000 of 1746 papers

Title	Date	Tasks	Status
The Surprising Harmfulness of Benign Overfitting for Adversarial Robustness	Jan 19, 2024	Adversarial Robustness	—Unverified
Label Smoothing and Adversarial Robustness	Sep 17, 2020	Adversarial AttackAdversarial Robustness	—Unverified
Label Smoothing and Logit Squeezing: A Replacement for Adversarial Training?	Oct 25, 2019	Adversarial Robustness	—Unverified
The Sword of Damocles in ViTs: Computational Redundancy Amplifies Adversarial Transferability	Apr 15, 2025	Adversarial Robustness	—Unverified
Achieving Adversarial Robustness Requires An Active Teacher	Dec 14, 2020	Adversarial Robustness	—Unverified
Large Language Model Sentinel: LLM Agent for Adversarial Purification	May 24, 2024	Adversarial DefenseAdversarial Purification	—Unverified
An Experimental Study of Semantic Continuity for Deep Learning Models	Nov 19, 2020	Adversarial RobustnessDeep Learning	—Unverified
Wavelets Beat Monkeys at Adversarial Robustness	Apr 19, 2023	Adversarial AttackAdversarial Robustness	—Unverified
Adversarial Attacks on Machine Learning in Embedded and IoT Platforms	Mar 3, 2023	Adversarial RobustnessModel Compression	—Unverified
Tightening the Approximation Error of Adversarial Risk with Auto Loss Function Search	Nov 9, 2021	Adversarial RobustnessAutoML	—Unverified
Layer-wise Learning of Stochastic Neural Networks with Information Bottleneck	Dec 4, 2017	Adversarial Robustness	—Unverified
An Ensemble Approach Towards Adversarial Robustness	Jun 10, 2021	Adversarial RobustnessRobust classification	—Unverified
An Empirical Study towards Characterizing Deep Learning Development and Deployment across Different Frameworks and Platforms	Sep 15, 2019	Adversarial AttackAdversarial Robustness	—Unverified
Universal Adversarial Framework to Improve Adversarial Robustness for Diabetic Retinopathy Detection	Dec 13, 2023	Adversarial RobustnessDiabetic Retinopathy Detection	—Unverified
TIMA: Text-Image Mutual Awareness for Balancing Zero-Shot Adversarial Robustness and Generalization Ability	May 27, 2024	Adversarial RobustnessKnowledge Distillation	—Unverified
Adversarial Attacks on Hyperbolic Networks	Dec 2, 2024	Adversarial Robustness	—Unverified
Lyapunov-Stable Deep Equilibrium Models	Apr 25, 2023	Adversarial DefenseAdversarial Robustness	—Unverified
Learning Lipschitz Feedback Policies from Expert Demonstrations: Closed-Loop Guarantees, Generalization and Robustness	Mar 30, 2021	Adversarial RobustnessImitation Learning	—Unverified
Learning Sample Reweighting for Accuracy and Adversarial Robustness	Oct 20, 2022	Adversarial RobustnessBilevel Optimization	—Unverified
Learning Sample Reweighting for Adversarial Robustness	Sep 29, 2021	Adversarial RobustnessBilevel Optimization	—Unverified
Tools and Practices for Responsible AI Engineering	Jan 14, 2022	Adversarial Robustness	—Unverified
Less is More: Feature Selection for Adversarial Robustness with Compressive Counter-Adversarial Attacks	Jun 18, 2021	Adversarial Robustnessfeature selection	—Unverified
Lessons from Defending Gemini Against Indirect Prompt Injections	May 20, 2025	Adversarial Robustness	—Unverified
Multimodal Adversarial Defense for Vision-Language Models by Leveraging One-To-Many Relationships	May 29, 2024	Adversarial DefenseAdversarial Robustness	—Unverified
Life-Cycle Routing Vulnerabilities of LLM Router	Mar 9, 2025	Adversarial Robustness	—Unverified

Show:10 25 50

← PrevPage 40 of 70Next →

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