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 1101–1125 of 1746 papers

Title	Date	Tasks	Status
Towards Robustness of Deep Neural Networks via Regularization	Jan 1, 2021	Adversarial AttackAdversarial Robustness	—Unverified
Adversarial Robustness of Deep Sensor Fusion Models	Jun 23, 2020	2D Object DetectionAdversarial Robustness	—Unverified
Towards Robust Vision Transformer via Masked Adaptive Ensemble	Jul 22, 2024	Adversarial Robustness	—Unverified
Towards Stable and Robust AdderNets	Dec 1, 2021	Adversarial Robustness	—Unverified
Towards Sustainable SecureML: Quantifying Carbon Footprint of Adversarial Machine Learning	Mar 27, 2024	Adversarial Robustness	—Unverified
Towards the Memorization Effect of Neural Networks in Adversarial Training	Jun 9, 2021	Adversarial RobustnessMemorization	—Unverified
Towards Trustworthy and Aligned Machine Learning: A Data-centric Survey with Causality Perspectives	Jul 31, 2023	Adversarial RobustnessFairness	—Unverified
Towards Understanding and Improving Adversarial Robustness of Vision Transformers	Jan 1, 2024	Adversarial Robustness	—Unverified
Towards Understanding the Regularization of Adversarial Robustness on Neural Networks	Nov 15, 2020	Adversarial Robustness	—Unverified
Towards unlocking the mystery of adversarial fragility of neural networks	Jun 23, 2024	Adversarial RobustnessClassification	—Unverified
Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networks	Jul 27, 2022	Adversarial RobustnessBenchmarking	—Unverified
Trace-Norm Adversarial Examples	Jul 2, 2020	Adversarial Robustness	—Unverified
Trading Inference-Time Compute for Adversarial Robustness	Jan 31, 2025	Adversarial Robustness	—Unverified
Training Robust Deep Neural Networks via Adversarial Noise Propagation	Sep 19, 2019	Adversarial DefenseAdversarial Robustness	—Unverified
Training Safe Neural Networks with Global SDP Bounds	Sep 15, 2024	Adversarial Robustness	—Unverified
Towards Model-Agnostic Adversarial Defenses using Adversarially Trained Autoencoders	Sep 12, 2019	Adversarial DefenseAdversarial Robustness	—Unverified
Transfer of Adversarial Robustness Between Perturbation Types	May 3, 2019	Adversarial Robustness	—Unverified
Transgressing the boundaries: towards a rigorous understanding of deep learning and its (non-)robustness	Jul 5, 2023	Adversarial RobustnessLearning Theory	—Unverified
Two Heads are Better than One: Towards Better Adversarial Robustness by Combining Transduction and Rejection	May 27, 2023	Adversarial Robustness	—Unverified
Two is Better than One: Efficient Ensemble Defense for Robust and Compact Models	Apr 7, 2025	Adversarial RobustnessDiversity	—Unverified
UFO-BLO: Unbiased First-Order Bilevel Optimization	Jun 5, 2020	Adversarial RobustnessBilevel Optimization	—Unverified
Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks	Feb 4, 2025	Adversarial RobustnessAutonomous Driving	—Unverified
Understanding Adversarial Behavior of DNNs by Disentangling Non-Robust and Robust Components in Performance Metric	Jun 6, 2019	Adversarial Robustness	—Unverified
Understanding Adversarially Robust Generalization via Weight-Curvature Index	Oct 10, 2024	Adversarial RobustnessGeneralization Bounds	—Unverified
Understanding Adversarial Robustness: The Trade-off between Minimum and Average Margin	Jul 26, 2019	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