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 451–475 of 1746 papers

Title	Date	Tasks	Status	Score
Generative Max-Mahalanobis Classifiers for Image Classification, Generation and More	Jan 1, 2021	Adversarial RobustnessClassification	CodeCode Available	5
Beyond One-Hot-Encoding: Injecting Semantics to Drive Image Classifiers	Aug 1, 2023	Adversarial Robustnessimage-classification	CodeCode Available	5
Beyond Model Interpretability: On the Faithfulness and Adversarial Robustness of Contrastive Textual Explanations	Oct 17, 2022	Adversarial AttackAdversarial Robustness	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
Adversarial Robustness of VAEs across Intersectional Subgroups	Jul 4, 2024	Adversarial RobustnessDimensionality Reduction	CodeCode Available	5
Adversarial Attack Generation Empowered by Min-Max Optimization	Jun 9, 2019	Adversarial AttackAdversarial Robustness	CodeCode Available	5
Finding Biological Plausibility for Adversarially Robust Features via Metameric Tasks	Feb 2, 2022	Adversarial RobustnessTexture Synthesis	CodeCode Available	5
Role of Spatial Context in Adversarial Robustness for Object Detection	Sep 30, 2019	Adversarial AttackAdversarial Robustness	CodeCode Available	5
Simple Post-Training Robustness Using Test Time Augmentations and Random Forest	Sep 16, 2021	Adversarial RobustnessDiversity	CodeCode Available	5
Enhancing Robustness in Incremental Learning with Adversarial Training	Dec 6, 2023	Adversarial Robustnessclass-incremental learning	CodeCode Available	5
Get Fooled for the Right Reason: Improving Adversarial Robustness through a Teacher-guided Curriculum Learning Approach	Oct 30, 2021	Adversarial Robustness	CodeCode Available	5
Adversarial Robustness of Supervised Sparse Coding	Oct 22, 2020	Adversarial Robustness	CodeCode Available	5
Feature Denoising for Improving Adversarial Robustness	Dec 9, 2018	Adversarial DefenseAdversarial Robustness	CodeCode Available	5
Clustering Effect of (Linearized) Adversarial Robust Models	Nov 25, 2021	Adversarial RobustnessClustering	CodeCode Available	5
Squeeze Training for Adversarial Robustness	May 23, 2022	Adversarial Robustness	CodeCode Available	5
Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack	Jun 15, 2022	Adversarial RobustnessComputational Efficiency	CodeCode Available	5
Benchmarking Robust Self-Supervised Learning Across Diverse Downstream Tasks	Jul 17, 2024	Adversarial RobustnessBenchmarking	CodeCode Available	5
Benchmarking Adversarial Robustness to Bias Elicitation in Large Language Models: Scalable Automated Assessment with LLM-as-a-Judge	Apr 10, 2025	Adversarial RobustnessBenchmarking	CodeCode Available	5
Adversarial Robustness of Stabilized NeuralODEs Might be from Obfuscated Gradients	Sep 28, 2020	Adversarial DefenseAdversarial Robustness	CodeCode Available	5
Improving Robustness with Adaptive Weight Decay	Sep 30, 2022	Adversarial Robustness	CodeCode Available	5
Fast Adversarial Training with Smooth Convergence	Aug 24, 2023	Adversarial Robustness	CodeCode Available	5
Feature Statistics with Uncertainty Help Adversarial Robustness	Mar 26, 2025	Adversarial Robustness	CodeCode Available	5
Confidence-aware Denoised Fine-tuning of Off-the-shelf Models for Certified Robustness	Nov 13, 2024	Adversarial RobustnessDenoising	CodeCode Available	5
FairDeFace: Evaluating the Fairness and Adversarial Robustness of Face Obfuscation Methods	Mar 11, 2025	Adversarial RobustnessFace Detection	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