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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 271–280 of 1746 papers

Title	Date	Tasks	Status	Hype
Towards Robust Vision Transformer via Masked Adaptive Ensemble	Jul 22, 2024	Adversarial Robustness	—Unverified	0
Beyond Dropout: Robust Convolutional Neural Networks Based on Local Feature Masking	Jul 18, 2024	Adversarial AttackAdversarial Robustness	—Unverified	0
Benchmarking Robust Self-Supervised Learning Across Diverse Downstream Tasks	Jul 17, 2024	Adversarial RobustnessBenchmarking	CodeCode Available	0
SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge	Jul 16, 2024	Adversarial RobustnessData Augmentation	CodeCode Available	1
Relaxing Graph Transformers for Adversarial Attacks	Jul 16, 2024	Adversarial RobustnessGraph Classification	—Unverified	0
Variational Randomized Smoothing for Sample-Wise Adversarial Robustness	Jul 16, 2024	Adversarial Robustness	—Unverified	0
Towards Adversarially Robust Vision-Language Models: Insights from Design Choices and Prompt Formatting Techniques	Jul 15, 2024	Adversarial Robustness	—Unverified	0
PartImageNet++ Dataset: Scaling up Part-based Models for Robust Recognition	Jul 15, 2024	Adversarial RobustnessInductive Bias	CodeCode Available	1
Evaluating the Adversarial Robustness of Semantic Segmentation: Trying Harder Pays Off	Jul 12, 2024	Adversarial Robustnessimage-classification	CodeCode Available	0
Deep Adversarial Defense Against Multilevel-Lp Attacks	Jul 12, 2024	Adversarial DefenseAdversarial Robustness	—Unverified	0

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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