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 1051–1075 of 1746 papers

Title	Date	Tasks	Status
MMARD: Improving the Min-Max Optimization Process in Adversarial Robustness Distillation	Mar 9, 2025	Adversarial Robustness	—Unverified
MMDT: Decoding the Trustworthiness and Safety of Multimodal Foundation Models	Mar 19, 2025	Adversarial RobustnessAutonomous Driving	—Unverified
A More Biologically Plausible Local Learning Rule for ANNs	Nov 24, 2020	Adversarial RobustnessBinary Classification	—Unverified
Towards Adversarially Robust Deep Image Denoising	Jan 12, 2022	Adversarial AttackAdversarial Robustness	—Unverified
Model-Agnostic Meta-Attack: Towards Reliable Evaluation of Adversarial Robustness	Oct 13, 2021	Adversarial Robustness	—Unverified
A margin-based replacement for cross-entropy loss	Jan 21, 2025	Adversarial RobustnessContinual Learning	—Unverified
Out of Thin Air: Exploring Data-Free Adversarial Robustness Distillation	Mar 21, 2023	Adversarial RobustnessKnowledge Distillation	—Unverified
Towards Adversarially Robust Text Classifiers by Learning to Reweight Clean Examples	May 1, 2022	Adversarial Robustness	—Unverified
Model Unlearning via Sparse Autoencoder Subspace Guided Projections	May 30, 2025	Adversarial Robustnessfeature selection	—Unverified
MOREL: Enhancing Adversarial Robustness through Multi-Objective Representation Learning	Oct 2, 2024	Adversarial RobustnessRepresentation Learning	—Unverified
Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications	May 26, 2025	Adversarial RobustnessIntrusion Detection	—Unverified
Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends	Apr 21, 2025	Adversarial RobustnessDecision Making	—Unverified
ALMA: Aggregated Lipschitz Maximization Attack on Auto-encoders	May 6, 2025	Adversarial Robustness	—Unverified
Towards Adversarially Robust Vision-Language Models: Insights from Design Choices and Prompt Formatting Techniques	Jul 15, 2024	Adversarial Robustness	—Unverified
Multi-Scale Architectures Matter: On the Adversarial Robustness of Flow-based Lossless Compression	Aug 26, 2022	Adversarial RobustnessDensity Estimation	—Unverified
Algorithmic Bias and Data Bias: Understanding the Relation between Distributionally Robust Optimization and Data Curation	Jun 17, 2021	Adversarial RobustnessFairness	—Unverified
Multi-stage Optimization based Adversarial Training	Jun 26, 2021	Adversarial Robustness	—Unverified
aiXamine: Simplified LLM Safety and Security	Apr 21, 2025	2kAdversarial Robustness	—Unverified
Multi-view Representation Learning from Malware to Defend Against Adversarial Variants	Oct 25, 2022	Adversarial RobustnessMULTI-VIEW LEARNING	—Unverified
NAP-Tuning: Neural Augmented Prompt Tuning for Adversarially Robust Vision-Language Models	Jun 15, 2025	Adversarial Robustness	—Unverified
Narrowing Class-Wise Robustness Gaps in Adversarial Training	Mar 20, 2025	Adversarial RobustnessClass-Specific Performance	—Unverified
Nash Equilibria and Pitfalls of Adversarial Training in Adversarial Robustness Games	Oct 23, 2022	Adversarial Robustness	—Unverified
Nearly Solved? Robust Deepfake Detection Requires More than Visual Forensics	Dec 7, 2024	Adversarial RobustnessDeepFake Detection	—Unverified
AI-Compass: A Comprehensive and Effective Multi-module Testing Tool for AI Systems	Nov 9, 2024	Adversarial Robustnessimage-classification	—Unverified
Neural Architecture Dilation for Adversarial Robustness	Aug 16, 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