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 1301–1325 of 1746 papers

Title	Date	Tasks	Status
Adversarial Robustness of Deep Neural Networks: A Survey from a Formal Verification Perspective	Jun 24, 2022	Adversarial RobustnessBIG-bench Machine Learning	—Unverified
Towards Robust Vision Transformer via Masked Adaptive Ensemble	Jul 22, 2024	Adversarial Robustness	—Unverified
Reframing Neural Networks: Deep Structure in Overcomplete Representations	Mar 10, 2021	Adversarial RobustnessModel Selection	—Unverified
A Curious Case of Remarkable Resilience to Gradient Attacks via Fully Convolutional and Differentiable Front End with a Skip Connection	Feb 26, 2024	Adversarial Robustness	—Unverified
A Robust Defense against Adversarial Attacks on Deep Learning-based Malware Detectors via (De)Randomized Smoothing	Feb 23, 2024	Adversarial Robustness	—Unverified
Reinforced Compressive Neural Architecture Search for Versatile Adversarial Robustness	Jun 10, 2024	Adversarial RobustnessNeural Architecture Search	—Unverified
Reinforcing Adversarial Robustness using Model Confidence Induced by Adversarial Training	Nov 21, 2017	Adversarial Robustness	—Unverified
Relating Adversarially Robust Generalization to Flat Minima	Apr 9, 2021	Adversarial Robustness	—Unverified
Relationship between Model Compression and Adversarial Robustness: A Review of Current Evidence	Nov 27, 2023	Adversarial RobustnessModel Compression	—Unverified
Relaxing Graph Transformers for Adversarial Attacks	Jul 16, 2024	Adversarial RobustnessGraph Classification	—Unverified
Releasing Inequality Phenomena in L_-Adversarial Training via Input Gradient Distillation	May 16, 2023	Adversarial DefenseAdversarial Robustness	—Unverified
Adversarial Robustness of Deep Learning: Theory, Algorithms, and Applications	Aug 24, 2021	Adversarial RobustnessDeep Learning	—Unverified
Reliable and Efficient Evaluation of Adversarial Robustness for Deep Hashing-Based Retrieval	Mar 22, 2023	Adversarial RobustnessDeep Hashing	—Unverified
Adversarial Robustness May Be at Odds With Simplicity	Jan 2, 2019	Adversarial RobustnessClassification	—Unverified
Towards Stable and Robust AdderNets	Dec 1, 2021	Adversarial Robustness	—Unverified
Removing Adversarial Noise in Class Activation Feature Space	Apr 19, 2021	Adversarial RobustnessDenoising	—Unverified
Adversarial Robustness is at Odds with Lazy Training	Jun 18, 2022	Adversarial RobustnessLearning Theory	—Unverified
Removing Out-of-Distribution Data Improves Adversarial Robustness	Nov 16, 2021	Adversarial Robustness	—Unverified
Towards Sustainable SecureML: Quantifying Carbon Footprint of Adversarial Machine Learning	Mar 27, 2024	Adversarial Robustness	—Unverified
XploreNAS: Explore Adversarially Robust & Hardware-efficient Neural Architectures for Non-ideal Xbars	Feb 15, 2023	Adversarial RobustnessNeural Architecture Search	—Unverified
Rerouting LLM Routers	Jan 3, 2025	Adversarial Robustness	—Unverified
Residual Error: a New Performance Measure for Adversarial Robustness	Jun 18, 2021	Adversarial Robustnessimage-classification	—Unverified
Resilience to Multiple Attacks via Adversarially Trained MIMO Ensembles	Sep 29, 2021	Adversarial Robustness	—Unverified
Revisiting and Advancing Adversarial Training Through A Simple Baseline	Jun 13, 2023	Adversarial DefenseAdversarial Robustness	—Unverified
Adversarial Robustness in Unsupervised Machine Learning: A Systematic Review	Jun 1, 2023	Adversarial RobustnessSystematic Literature Review	—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