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 601–625 of 1746 papers

Title	Date	Tasks	Status
A Study on the Uncertainty of Convolutional Layers in Deep Neural Networks	Nov 27, 2020	Adversarial Robustness	—Unverified
Adversarial Robustness of Deep Learning: Theory, Algorithms, and Applications	Aug 24, 2021	Adversarial RobustnessDeep Learning	—Unverified
Few-Shot Adversarial Low-Rank Fine-Tuning of Vision-Language Models	May 21, 2025	Adversarial Robustnessparameter-efficient fine-tuning	—Unverified
Finding a human-like classifier	Nov 13, 2019	Adversarial RobustnessContinual Learning	—Unverified
Formalizing Generalization and Adversarial Robustness of Neural Networks to Weight Perturbations	Dec 1, 2021	Adversarial RobustnessModel Compression	—Unverified
Generalizability of Adversarial Robustness Under Distribution Shifts	Sep 29, 2022	Adversarial RobustnessDomain Generalization	—Unverified
Feature Losses for Adversarial Robustness	Dec 10, 2019	Adversarial RobustnessDenoising	—Unverified
Adaptive Batch Normalization Networks for Adversarial Robustness	May 20, 2024	Adversarial DefenseAdversarial Robustness	—Unverified
Feature Prioritization and Regularization Improve Standard Accuracy and Adversarial Robustness	Oct 4, 2018	Adversarial RobustnessDenoising	—Unverified
Associative Adversarial Learning Based on Selective Attack	Dec 28, 2021	Adversarial RobustnessFew-Shot Learning	—Unverified
Non-adversarial Robustness of Deep Learning Methods for Computer Vision	May 24, 2023	Adversarial RobustnessDeep Learning	—Unverified
Assessing the Adversarial Robustness of Monte Carlo and Distillation Methods for Deep Bayesian Neural Network Classification	Feb 7, 2020	Adversarial RobustnessGeneral Classification	—Unverified
Assessing Adversarial Robustness of Large Language Models: An Empirical Study	May 4, 2024	Adversarial Robustnesstext-classification	—Unverified
Adversarial Robustness May Be at Odds With Simplicity	Jan 2, 2019	Adversarial RobustnessClassification	—Unverified
A Spectral Perspective towards Understanding and Improving Adversarial Robustness	Jun 25, 2023	Adversarial Robustness	—Unverified
Ensemble Adversarial Defense via Integration of Multiple Dispersed Low Curvature Models	Mar 25, 2024	Adversarial DefenseAdversarial Robustness	—Unverified
Improving Transformation-based Defenses against Adversarial Examples with First-order Perturbations	Mar 8, 2021	Adversarial Robustness	—Unverified
Adversarial examples attack based on random warm restart mechanism and improved Nesterov momentum	May 10, 2021	Adversarial RobustnessDeep Learning	—Unverified
Feature Distillation With Guided Adversarial Contrastive Learning	Sep 21, 2020	Adversarial RobustnessContrastive Learning	—Unverified
Enhancing the "Immunity" of Mixture-of-Experts Networks for Adversarial Defense	Feb 29, 2024	Adversarial DefenseAdversarial Robustness	—Unverified
Enhancing the Antidote: Improved Pointwise Certifications against Poisoning Attacks	Aug 15, 2023	Adversarial Robustness	—Unverified
A Simple Framework to Enhance the Adversarial Robustness of Deep Learning-based Intrusion Detection System	Dec 6, 2023	Adversarial AttackAdversarial Robustness	—Unverified
Enhancing Quantum Adversarial Robustness by Randomized Encodings	Dec 5, 2022	Adversarial RobustnessQuantum Machine Learning	—Unverified
A Simple Fine-tuning Is All You Need: Towards Robust Deep Learning Via Adversarial Fine-tuning	Dec 25, 2020	Adversarial RobustnessAll	—Unverified
Adversarial Robustness is at Odds with Lazy Training	Jun 18, 2022	Adversarial RobustnessLearning Theory	—Unverified

Show:10 25 50

← PrevPage 25 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