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 826–850 of 1746 papers

Title	Date	Tasks	Status
Complexity Matters: Effective Dimensionality as a Measure for Adversarial Robustness	Oct 24, 2024	Adversarial RobustnessModel Selection	—Unverified
Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks	Feb 6, 2023	Adversarial RobustnessImage Segmentation	—Unverified
Collective Robustness Certificates	Jan 1, 2021	Adversarial RobustnessImage Segmentation	—Unverified
A Geometrical Approach to Evaluate the Adversarial Robustness of Deep Neural Networks	Oct 10, 2023	Adversarial Robustness	—Unverified
Clustering Effect of Adversarial Robust Models	Dec 1, 2021	Adversarial RobustnessClustering	—Unverified
A Fundamental Accuracy--Robustness Trade-off in Regression and Classification	Nov 6, 2024	Adversarial Robustnessregression	—Unverified
A Frequency Perspective of Adversarial Robustness	Oct 26, 2021	Adversarial Robustness	—Unverified
Adversarial Prompt Distillation for Vision-Language Models	Nov 22, 2024	Adversarial RobustnessAutonomous Driving	—Unverified
A Framework for Verification of Wasserstein Adversarial Robustness	Oct 13, 2021	Adversarial AttackAdversarial Robustness	—Unverified
Classifier Guidance Enhances Diffusion-based Adversarial Purification by Preserving Predictive Information	Aug 12, 2024	Adversarial PurificationAdversarial Robustness	—Unverified
Class-Aware Robust Adversarial Training for Object Detection	Mar 30, 2021	Adversarial RobustnessObject	—Unverified
A Flat Minima Perspective on Understanding Augmentations and Model Robustness	May 30, 2025	Adversarial RobustnessData Augmentation	—Unverified
Class-Aware Domain Adaptation for Improving Adversarial Robustness	May 10, 2020	Adversarial AttackAdversarial Defense	—Unverified
A Finer Calibration Analysis for Adversarial Robustness	May 4, 2021	Adversarial RobustnessBIG-bench Machine Learning	—Unverified
Characterizing the adversarial vulnerability of speech self-supervised learning	Nov 8, 2021	Adversarial RobustnessBenchmarking	—Unverified
Affine-Invariant Robust Training	Oct 8, 2020	Adversarial RobustnessBIG-bench Machine Learning	—Unverified
Adversarial Amendment is the Only Force Capable of Transforming an Enemy into a Friend	May 18, 2023	Adversarial AttackAdversarial Defense	—Unverified
Chaos Theory and Adversarial Robustness	Oct 20, 2022	Adversarial Robustness	—Unverified
CGDTest: A Constrained Gradient Descent Algorithm for Testing Neural Networks	Apr 4, 2023	Adversarial RobustnessDNN Testing	—Unverified
Certifying Robustness of Graph Laplacian Based Semi-Supervised Learning	Jan 1, 2021	Adversarial Robustness	—Unverified
Certifying Adapters: Enabling and Enhancing the Certification of Classifier Adversarial Robustness	May 25, 2024	Adversarial RobustnessData Augmentation	—Unverified
AdvFilter: Predictive Perturbation-aware Filtering against Adversarial Attack via Multi-domain Learning	Jul 14, 2021	Adversarial AttackAdversarial Robustness	—Unverified
Adversarial Masked Autoencoder Purifier with Defense Transferability	Jan 28, 2025	Adversarial DefenseAdversarial Robustness	—Unverified
Adversarial Alignment for LLMs Requires Simpler, Reproducible, and More Measurable Objectives	Feb 17, 2025	Adversarial Robustness	—Unverified
A Closer Look at the Adversarial Robustness of Information Bottleneck Models	Jul 12, 2021	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