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 526–550 of 1746 papers

Title	Date	Tasks	Status
Exploiting the Relationship Between Kendall's Rank Correlation and Cosine Similarity for Attribution Protection	May 15, 2022	Adversarial Robustness	—Unverified
Adversarially Robust 3D Point Cloud Recognition Using Self-Supervisions	Dec 1, 2021	Adversarial RobustnessAutonomous Driving	—Unverified
Beyond Empirical Risk Minimization: Local Structure Preserving Regularization for Improving Adversarial Robustness	Mar 29, 2023	Adversarial Robustnessimage-classification	—Unverified
Beyond Dropout: Robust Convolutional Neural Networks Based on Local Feature Masking	Jul 18, 2024	Adversarial AttackAdversarial Robustness	—Unverified
Adversarial Robustness on Image Classification with k-means	Dec 15, 2023	Adversarial RobustnessClassification	—Unverified
Beyond cross-entropy: learning highly separable feature distributions for robust and accurate classification	Oct 29, 2020	Adversarial RobustnessGeneral Classification	—Unverified
Beyond Classification: Evaluating Diffusion Denoised Smoothing for Security-Utility Trade off	May 21, 2025	Adversarial AttackAdversarial Robustness	—Unverified
Adversarial Robustness of Visual Dialog	Jul 6, 2022	Adversarial RobustnessVisual Dialog	—Unverified
Mitigating the Impact of Noisy Edges on Graph-Based Algorithms via Adversarial Robustness Evaluation	Jan 28, 2024	Adversarial AttackAdversarial Robustness	—Unverified
Experimental robustness benchmark of quantum neural network on a superconducting quantum processor	May 22, 2025	Adversarial AttackAdversarial Robustness	—Unverified
Explicit Tradeoffs between Adversarial and Natural Distributional Robustness	Sep 15, 2022	Adversarial Robustness	—Unverified
Exploration by Optimization with Hybrid Regularizers: Logarithmic Regret with Adversarial Robustness in Partial Monitoring	Feb 13, 2024	Adversarial RobustnessDecision Making	—Unverified
Better Representations via Adversarial Training in Pre-Training: A Theoretical Perspective	Jan 26, 2024	Adversarial RobustnessContrastive Learning	—Unverified
Better Generalization with Adaptive Adversarial Training	May 28, 2019	Adversarial Robustness	—Unverified
Adversarial Learning with Cost-Sensitive Classes	Jan 29, 2021	Adversarial Robustness	—Unverified
Benchmarking the Physical-world Adversarial Robustness of Vehicle Detection	Apr 11, 2023	Adversarial AttackAdversarial Robustness	—Unverified
Adversarial Robustness of Streaming Algorithms through Importance Sampling	Jun 28, 2021	Adversarial RobustnessClustering	—Unverified
Evaluation Methodology for Attacks Against Confidence Thresholding Models	May 1, 2019	Adversarial RobustnessBenchmarking	—Unverified
Evolutionary Reinforcement Learning: A Systematic Review and Future Directions	Feb 20, 2024	Adversarial RobustnessEvolutionary Algorithms	—Unverified
Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks	Apr 28, 2020	Adversarial Robustness	—Unverified
Adversarial Purification with the Manifold Hypothesis	Oct 26, 2022	Adversarial PurificationAdversarial Robustness	—Unverified
Benchmarking Adversarial Robustness of Image Shadow Removal with Shadow-adaptive Attacks	Mar 15, 2024	Adversarial AttackAdversarial Robustness	—Unverified
Adversarial robustness of sparse local Lipschitz predictors	Feb 26, 2022	Adversarial Robustness	—Unverified
Evaluating the Evaluators: Trust in Adversarial Robustness Tests	Jul 4, 2025	Adversarial Robustness	—Unverified
Benchmarking Adversarial Robustness of Compressed Deep Learning Models	Aug 16, 2023	Adversarial RobustnessBenchmarking	—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