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 876–900 of 1746 papers

Title	Date	Tasks	Status
Improving Adversarial Robustness for Free with Snapshot Ensemble	Oct 7, 2021	Adversarial Robustness	—Unverified
A Survey and Evaluation of Adversarial Attacks for Object Detection	Aug 4, 2024	Adversarial RobustnessAutonomous Vehicles	—Unverified
Improving Adversarial Robustness in Weight-quantized Neural Networks	Dec 29, 2020	Adversarial RobustnessQuantization	—Unverified
Improving adversarial robustness of deep neural networks by using semantic information	Aug 18, 2020	Adversarial AttackAdversarial Robustness	—Unverified
Test-Time Adaptation and Adversarial Robustness	Jan 1, 2021	Adversarial RobustnessDomain Adaptation	—Unverified
Improving Adversarial Robustness of Ensembles with Diversity Training	Jan 28, 2019	Adversarial RobustnessDiversity	—Unverified
A Simple Framework to Enhance the Adversarial Robustness of Deep Learning-based Intrusion Detection System	Dec 6, 2023	Adversarial AttackAdversarial Robustness	—Unverified
A Simple Fine-tuning Is All You Need: Towards Robust Deep Learning Via Adversarial Fine-tuning	Dec 25, 2020	Adversarial RobustnessAll	—Unverified
SAT: Improving Adversarial Training via Curriculum-Based Loss Smoothing	Mar 18, 2020	Adversarial Robustness	—Unverified
Improving Adversarial Robustness to Sensitivity and Invariance Attacks with Deep Metric Learning	Nov 4, 2022	Adversarial RobustnessMetric Learning	—Unverified
ASAT: Adaptively Scaled Adversarial Training in Time Series	Aug 20, 2021	Adversarial RobustnessTime Series	—Unverified
A Robust Adversarial Ensemble with Causal (Feature Interaction) Interpretations for Image Classification	Dec 28, 2024	Adversarial Robustnesscounterfactual	—Unverified
Improving Adversarial Robustness via Attention and Adversarial Logit Pairing	Aug 23, 2019	Adversarial Robustness	—Unverified
Improving Adversarial Robustness via Unlabeled Out-of-Domain Data	Jun 15, 2020	Adversarial RobustnessData Augmentation	—Unverified
Test-Time Adaptation with Perturbation Consistency Learning	Apr 25, 2023	Adversarial RobustnessPseudo Label	—Unverified
TETRIS: Towards Exploring the Robustness of Interactive Segmentation	Feb 9, 2024	Adversarial AttackAdversarial Robustness	—Unverified
Are Time-Series Foundation Models Deployment-Ready? A Systematic Study of Adversarial Robustness Across Domains	May 26, 2025	Adversarial RobustnessData Poisoning	—Unverified
Improving Adversarial Robustness via Feature Pattern Consistency Constraint	Jun 13, 2024	Adversarial Robustnessfeature selection	—Unverified
Adversarial Examples are Misaligned in Diffusion Model Manifolds	Jan 12, 2024	Adversarial RobustnessImage Inpainting	—Unverified
Adversarial Examples Are a Natural Consequence of Test Error in Noise	Jan 29, 2019	Adversarial Robustness	—Unverified
Are Perceptually-Aligned Gradients a General Property of Robust Classifiers?	Oct 18, 2019	Adversarial Robustness	—Unverified
Improving Adversarial Robustness via Phase and Amplitude-aware Prompting	Feb 6, 2025	Adversarial Robustness	—Unverified
Improving Adversarial Robustness with Hypersphere Embedding and Angular-based Regularizations	Mar 15, 2023	Adversarial Robustness	—Unverified
The Attack Generator: A Systematic Approach Towards Constructing Adversarial Attacks	Jun 17, 2019	Adversarial AttackAdversarial Robustness	—Unverified
Improving Adversarial Transferability of Vision-Language Pre-training Models through Collaborative Multimodal Interaction	Mar 16, 2024	Adversarial RobustnessImage-text Retrieval	—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