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 801–825 of 1746 papers

Title	Date	Tasks	Status
A Comparative Analysis of Adversarial Robustness for Quantum and Classical Machine Learning Models	Apr 24, 2024	Adversarial RobustnessQuantum Machine Learning	—Unverified
Curvature Dynamic Black-box Attack: revisiting adversarial robustness via dynamic curvature estimation	May 25, 2025	Adversarial AttackAdversarial Robustness	—Unverified
A More Biologically Plausible Local Learning Rule for ANNs	Nov 24, 2020	Adversarial RobustnessBinary Classification	—Unverified
CSTAR: Towards Compact and STructured Deep Neural Networks with Adversarial Robustness	Dec 4, 2022	Adversarial RobustnessModel Compression	—Unverified
Cross-Entropy Loss Functions: Theoretical Analysis and Applications	Apr 14, 2023	Adversarial Robustness	—Unverified
A margin-based replacement for cross-entropy loss	Jan 21, 2025	Adversarial RobustnessContinual Learning	—Unverified
Cross Domain Generative Augmentation: Domain Generalization with Latent Diffusion Models	Dec 8, 2023	Adversarial RobustnessData Augmentation	—Unverified
Criticality Leveraged Adversarial Training (CLAT) for Boosted Performance via Parameter Efficiency	Aug 19, 2024	Adversarial Robustness	—Unverified
Corruption-Robust Offline Reinforcement Learning	Jun 11, 2021	Adversarial RobustnessOffline RL	—Unverified
ALMA: Aggregated Lipschitz Maximization Attack on Auto-encoders	May 6, 2025	Adversarial Robustness	—Unverified
Contextual Fusion For Adversarial Robustness	Nov 18, 2020	Adversarial Robustness	—Unverified
Constraining Logits by Bounded Function for Adversarial Robustness	Oct 6, 2020	Adversarial Robustness	—Unverified
Constrained Learning with Non-Convex Losses	Mar 8, 2021	Adversarial RobustnessFairness	—Unverified
Constrained Adaptive Attacks: Realistic Evaluation of Adversarial Examples and Robust Training of Deep Neural Networks for Tabular Data	Nov 8, 2023	Adversarial Robustness	—Unverified
Algorithmic Bias and Data Bias: Understanding the Relation between Distributionally Robust Optimization and Data Curation	Jun 17, 2021	Adversarial RobustnessFairness	—Unverified
aiXamine: Simplified LLM Safety and Security	Apr 21, 2025	2kAdversarial Robustness	—Unverified
AI-Compass: A Comprehensive and Effective Multi-module Testing Tool for AI Systems	Nov 9, 2024	Adversarial Robustnessimage-classification	—Unverified
Adversarial Risk and the Dangers of Evaluating Against Weak Attacks	Feb 15, 2018	Adversarial Robustness	—Unverified
Adversarial Attacks and Defenses for Speech Recognition Systems	Mar 31, 2021	Adversarial RobustnessAutomatic Speech Recognition	—Unverified
CARES: Comprehensive Evaluation of Safety and Adversarial Robustness in Medical LLMs	May 16, 2025	Adversarial RobustnessSafety Alignment	—Unverified
Confronting the Reproducibility Crisis: A Case Study of Challenges in Cybersecurity AI	May 29, 2024	Adversarial Robustness	—Unverified
Conflict-Aware Adversarial Training	Oct 21, 2024	Adversarial Robustness	—Unverified
A Hybrid Defense Strategy for Boosting Adversarial Robustness in Vision-Language Models	Oct 18, 2024	Adversarial RobustnessAutonomous Driving	—Unverified
A Holistic Assessment of the Reliability of Machine Learning Systems	Jul 20, 2023	Adversarial RobustnessOut-of-Distribution Detection	—Unverified
A High Dimensional Statistical Model for Adversarial Training: Geometry and Trade-Offs	Feb 8, 2024	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