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 426–450 of 1746 papers

Title	Date	Tasks	Status
Causal Adversarial Perturbations for Individual Fairness and Robustness in Heterogeneous Data Spaces	Aug 17, 2023	Adversarial RobustnessFairness	—Unverified
Causal Fair Metric: Bridging Causality, Individual Fairness, and Adversarial Robustness	Oct 30, 2023	Adversarial Robustnesscounterfactual	—Unverified
Causal Feature Selection for Responsible Machine Learning	Feb 5, 2024	Adversarial RobustnessDomain Generalization	—Unverified
Causal Information Bottleneck Boosts Adversarial Robustness of Deep Neural Network	Oct 25, 2022	Adversarial RobustnessCausal Inference	—Unverified
CausalPC: Improving the Robustness of Point Cloud Classification by Causal Effect Identification	Jan 1, 2024	Adversarial RobustnessClassification	—Unverified
Adversarially Robust Neural Architectures	Sep 2, 2020	Adversarial AttackAdversarial Robustness	—Unverified
Do Wider Neural Networks Really Help Adversarial Robustness?	Oct 3, 2020	Adversarial Robustness	—Unverified
Do Gradient-based Explanations Tell Anything About Adversarial Robustness to Android Malware?	May 4, 2020	Adversarial RobustnessAndroid Malware Detection	—Unverified
Dynamical Low-Rank Compression of Neural Networks with Robustness under Adversarial Attacks	May 12, 2025	Adversarial RobustnessLow-rank compression	—Unverified
Distance-Restricted Explanations: Theoretical Underpinnings & Efficient Implementation	May 14, 2024	Adversarial RobustnessExplainable artificial intelligence	—Unverified
Certified Adversarial Robustness for Deep Reinforcement Learning	Oct 28, 2019	Adversarial RobustnessCollision Avoidance	—Unverified
Adversarial Robustness Unhardening via Backdoor Attacks in Federated Learning	Oct 17, 2023	Adversarial RobustnessFederated Learning	—Unverified
Distilled Agent DQN for Provable Adversarial Robustness	Sep 27, 2018	Adversarial Robustnessreinforcement-learning	—Unverified
Adversarially Robust Industrial Anomaly Detection Through Diffusion Model	Aug 9, 2024	Adversarial PurificationAdversarial Robustness	—Unverified
Distilling Adversarial Robustness Using Heterogeneous Teachers	Feb 23, 2024	Adversarial RobustnessKnowledge Distillation	—Unverified
Bridged Adversarial Training	Aug 25, 2021	Adversarial Robustness	—Unverified
AdPO: Enhancing the Adversarial Robustness of Large Vision-Language Models with Preference Optimization	Apr 2, 2025	Adversarial DefenseAdversarial Robustness	—Unverified
Training Graph Neural Networks Using Non-Robust Samples	Dec 19, 2024	Adversarial RobustnessGraph Neural Network	—Unverified
Boosting Certified Robustness for Time Series Classification with Efficient Self-Ensemble	Sep 4, 2024	Adversarial RobustnessTime Series	—Unverified
Adversarially Robust Few-shot Learning via Parameter Co-distillation of Similarity and Class Concept Learners	Jan 1, 2024	Adversarial RobustnessFew-Shot Learning	—Unverified
Certified Robustness to Word Substitution Attack with Differential Privacy	Jun 1, 2021	Adversarial RobustnessClassification	—Unverified
A Survey and Evaluation of Adversarial Attacks for Object Detection	Aug 4, 2024	Adversarial RobustnessAutonomous Vehicles	—Unverified
Boosting Barely Robust Learners: A New Perspective on Adversarial Robustness	Feb 11, 2022	Adversarial Robustness	—Unverified
AdvFilter: Predictive Perturbation-aware Filtering against Adversarial Attack via Multi-domain Learning	Jul 14, 2021	Adversarial AttackAdversarial Robustness	—Unverified
Disentangled Text Representation Learning with Information-Theoretic Perspective for Adversarial Robustness	Oct 26, 2022	Adversarial RobustnessRepresentation Learning	—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