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	Score
Generating Adversarial Examples with Adversarial Networks	Jan 8, 2018	Adversarial Robustness	CodeCode Available	5
On Adversarial Robustness of Point Cloud Semantic Segmentation	Dec 11, 2021	Adversarial RobustnessAutonomous Driving	CodeCode Available	5
Generating Adversarial Samples in Mini-Batches May Be Detrimental To Adversarial Robustness	Mar 30, 2023	Adversarial Robustness	CodeCode Available	5
Give me a hint: Can LLMs take a hint to solve math problems?	Oct 8, 2024	Adversarial RobustnessMath	CodeCode Available	5
GenAttack: Practical Black-box Attacks with Gradient-Free Optimization	May 28, 2018	Adversarial AttackAdversarial Robustness	CodeCode Available	5
Adaptive Randomized Smoothing: Certified Adversarial Robustness for Multi-Step Defences	Jun 14, 2024	Adversarial Robustnessimage-classification	CodeCode Available	5
Gated Information Bottleneck for Generalization in Sequential Environments	Oct 12, 2021	Adversarial RobustnessOut of Distribution (OOD) Detection	CodeCode Available	5
Adversarial Robustness of Deep Learning-Based Malware Detectors via (De)Randomized Smoothing	Apr 22, 2024	Adversarial Robustness	CodeCode Available	5
GAT: Guided Adversarial Training with Pareto-optimal Auxiliary Tasks	Feb 6, 2023	Adversarial RobustnessData Augmentation	CodeCode Available	5
Global-Local Regularization Via Distributional Robustness	Mar 1, 2022	Adversarial RobustnessDomain Adaptation	CodeCode Available	5
Adversarial Examples for k-Nearest Neighbor Classifiers Based on Higher-Order Voronoi Diagrams	Nov 19, 2020	Adversarial Robustness	CodeCode Available	5
A Training Rate and Survival Heuristic for Inference and Robustness Evaluation (TRASHFIRE)	Jan 24, 2024	Adversarial Robustness	CodeCode Available	5
Adversarial Robustness of Deep Learning Models for Inland Water Body Segmentation from SAR Images	May 3, 2025	Adversarial RobustnessData Poisoning	CodeCode Available	5
Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization	Nov 12, 2024	Adversarial RobustnessDeepFake Detection	CodeCode Available	5
FI-ODE: Certifiably Robust Forward Invariance in Neural ODEs	Oct 30, 2022	Adversarial Robustnesscontinuous-control	CodeCode Available	5
Efficiently Training Low-Curvature Neural Networks	Jun 14, 2022	Adversarial Robustness	CodeCode Available	5
Gradient-Free Adversarial Attacks for Bayesian Neural Networks	Dec 23, 2020	Adversarial RobustnessBayesian Inference	CodeCode Available	5
A Study on Adversarial Robustness of Discriminative Prototypical Learning	Apr 3, 2025	Adversarial Robustness	CodeCode Available	5
Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers	Jun 12, 2019	Adversarial Robustness	CodeCode Available	5
Adversarial robustness of VAEs through the lens of local geometry	Aug 8, 2022	Adversarial RobustnessDecoder	CodeCode Available	5
Weight-Covariance Alignment for Adversarially Robust Neural Networks	Oct 17, 2020	Adversarial Robustness	CodeCode Available	5
Adversarial Examples for Evaluating Math Word Problem Solvers	Sep 13, 2021	Adversarial RobustnessMath	CodeCode Available	5
Feature Statistics with Uncertainty Help Adversarial Robustness	Mar 26, 2025	Adversarial Robustness	CodeCode Available	5
Feature Denoising for Improving Adversarial Robustness	Dec 9, 2018	Adversarial DefenseAdversarial Robustness	CodeCode Available	5
Assaying Out-Of-Distribution Generalization in Transfer Learning	Jul 19, 2022	Adversarial RobustnessOut-of-Distribution Generalization	CodeCode Available	5

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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