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 1701–1725 of 1746 papers

Title	Date	Tasks	Status
Flooding-X: Improving BERT's Resistance to Adversarial Attacks via Loss-Restricted Fine-Tuning	Nov 16, 2021	Adversarial Robustnesstext-classification	—Unverified
CE-based white-box adversarial attacks will not work using super-fitting	May 4, 2022	Adversarial AttackAdversarial Robustness	—Unverified
BARReL: Bottleneck Attention for Adversarial Robustness in Vision-Based Reinforcement Learning	Aug 22, 2022	Adversarial Robustnessreinforcement-learning	—Unverified
FocusedCleaner: Sanitizing Poisoned Graphs for Robust GNN-based Node Classification	Oct 25, 2022	Adversarial RobustnessData Poisoning	—Unverified
Formalizing Generalization and Adversarial Robustness of Neural Networks to Weight Perturbations	Dec 1, 2021	Adversarial RobustnessModel Compression	—Unverified
Framework for Progressive Knowledge Fusion in Large Language Models Through Structured Conceptual Redundancy Analysis	Jan 23, 2025	Adversarial RobustnessModel Optimization	—Unverified
Backdoor Attacks Against Incremental Learners: An Empirical Evaluation Study	May 28, 2023	Adversarial RobustnessBackdoor Attack	—Unverified
Frequency Regularization for Improving Adversarial Robustness	Dec 24, 2022	Adversarial Robustness	—Unverified
From Environmental Sound Representation to Robustness of 2D CNN Models Against Adversarial Attacks	Apr 14, 2022	Adversarial AttackAdversarial Robustness	—Unverified
From Sound Representation to Model Robustness	Jul 27, 2020	Adversarial AttackAdversarial Robustness	—Unverified
FullLoRA-AT: Efficiently Boosting the Robustness of Pretrained Vision Transformers	Jan 3, 2024	Adversarial Robustness	—Unverified
Fully Dynamic Adversarially Robust Correlation Clustering in Polylogarithmic Update Time	Nov 15, 2024	Adversarial RobustnessClustering	—Unverified
FuncFooler: A Practical Black-box Attack Against Learning-based Binary Code Similarity Detection Methods	Aug 26, 2022	Adversarial RobustnessCode Generation	—Unverified
Functional Network: A Novel Framework for Interpretability of Deep Neural Networks	May 24, 2022	Adversarial RobustnessTopological Data Analysis	—Unverified
Function Composition in Trustworthy Machine Learning: Implementation Choices, Insights, and Questions	Feb 17, 2023	Adversarial RobustnessFairness	—Unverified
Function-Space Regularization for Deep Bayesian Classification	Jul 12, 2023	Adversarial RobustnessClassification	—Unverified
Function-Space Variational Inference for Deep Bayesian Classification	Sep 29, 2021	Adversarial RobustnessClassification	—Unverified
Game-Theoretic Defenses for Robust Conformal Prediction Against Adversarial Attacks in Medical Imaging	Nov 7, 2024	Adversarial RobustnessConformal Prediction	—Unverified
aw_nas: A Modularized and Extensible NAS framework	Nov 25, 2020	Adversarial RobustnessNeural Architecture Search	—Unverified
GARNET: A Spectral Approach to Robust and Scalable Graph Neural Networks	Sep 29, 2021	Adversarial RobustnessGraph Embedding	—Unverified
Average Margin Regularization for Classifiers	Oct 9, 2018	Adversarial Robustness	—Unverified
Standard-Deviation-Inspired Regularization for Improving Adversarial Robustness	Dec 27, 2024	Adversarial Robustness	—Unverified
STAR: Noisy Semi-Supervised Transfer Learning for Visual Classification	Aug 18, 2021	Adversarial RobustnessClassification	—Unverified
Stochastic Gradient Descent with Nonlinear Conjugate Gradient-Style Adaptive Momentum	Dec 3, 2020	Adversarial Robustness	—Unverified
General Coded Computing: Adversarial Settings	Feb 12, 2025	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