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 926–950 of 1746 papers

Title	Date	Tasks	Status
Reinforced Compressive Neural Architecture Search for Versatile Adversarial Robustness	Jun 10, 2024	Adversarial RobustnessNeural Architecture Search	—Unverified
Reinforcing Adversarial Robustness using Model Confidence Induced by Adversarial Training	Nov 21, 2017	Adversarial Robustness	—Unverified
Relating Adversarially Robust Generalization to Flat Minima	Apr 9, 2021	Adversarial Robustness	—Unverified
Relationship between Model Compression and Adversarial Robustness: A Review of Current Evidence	Nov 27, 2023	Adversarial RobustnessModel Compression	—Unverified
Releasing Inequality Phenomena in L_-Adversarial Training via Input Gradient Distillation	May 16, 2023	Adversarial DefenseAdversarial Robustness	—Unverified
Reliable and Efficient Evaluation of Adversarial Robustness for Deep Hashing-Based Retrieval	Mar 22, 2023	Adversarial RobustnessDeep Hashing	—Unverified
Removing Adversarial Noise in Class Activation Feature Space	Apr 19, 2021	Adversarial RobustnessDenoising	—Unverified
Removing Out-of-Distribution Data Improves Adversarial Robustness	Nov 16, 2021	Adversarial Robustness	—Unverified
Rerouting LLM Routers	Jan 3, 2025	Adversarial Robustness	—Unverified
Residual Error: a New Performance Measure for Adversarial Robustness	Jun 18, 2021	Adversarial Robustnessimage-classification	—Unverified
Resilience to Multiple Attacks via Adversarially Trained MIMO Ensembles	Sep 29, 2021	Adversarial Robustness	—Unverified
Revisiting and Advancing Adversarial Training Through A Simple Baseline	Jun 13, 2023	Adversarial DefenseAdversarial Robustness	—Unverified
Rethinking Audio-Visual Adversarial Vulnerability from Temporal and Modality Perspectives	Feb 17, 2025	Adversarial Robustnessaudio-visual learning	—Unverified
Rethinking Classifier and Adversarial Attack	May 4, 2022	Adversarial AttackAdversarial Robustness	—Unverified
Rethinking Feature Uncertainty in Stochastic Neural Networks for Adversarial Robustness	Jan 1, 2022	Adversarial Robustness	—Unverified
Rethinking Non-idealities in Memristive Crossbars for Adversarial Robustness in Neural Networks	Aug 25, 2020	Adversarial Robustness	—Unverified
Rethinking the Adversarial Robustness of Multi-Exit Neural Networks in an Attack-Defense Game	Jan 1, 2025	Adversarial Robustness	—Unverified
Revisiting Adversarial Robustness of Classifiers With a Reject Option	Nov 22, 2021	Adversarial Robustnessimage-classification	—Unverified
Revisiting Robustness in Graph Machine Learning	May 1, 2023	Adversarial Robustness	—Unverified
Revisiting Role of Autoencoders in Adversarial Settings	May 21, 2020	Adversarial DefenseAdversarial Robustness	—Unverified
Revisiting Semi-supervised Adversarial Robustness via Noise-aware Online Robust Distillation	Sep 19, 2024	Adversarial Robustness	—Unverified
Revisiting the Adversarial Robustness-Accuracy Tradeoff in Robot Learning	Apr 15, 2022	Adversarial RobustnessAutonomous Driving	—Unverified
Revisiting the Robust Generalization of Adversarial Prompt Tuning	May 18, 2024	Adversarial RobustnessPrompt Learning	—Unverified
Robust and differentially private stochastic linear bandits	Apr 23, 2023	Adversarial Robustness	—Unverified
Robust and Private Learning of Halfspaces	Nov 30, 2020	Adversarial 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