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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 291–300 of 1746 papers

Title	Date	Tasks	Status	Hype
Diffusion-based Adversarial Purification for Intrusion Detection	Jun 25, 2024	Adversarial PurificationAdversarial Robustness	CodeCode Available	0
Towards unlocking the mystery of adversarial fragility of neural networks	Jun 23, 2024	Adversarial RobustnessClassification	—Unverified	0
DataFreeShield: Defending Adversarial Attacks without Training Data	Jun 21, 2024	Adversarial RobustnessDataset Generation	—Unverified	0
Deciphering the Definition of Adversarial Robustness for post-hoc OOD Detectors	Jun 21, 2024	Adversarial DefenseAdversarial Robustness	—Unverified	0
Understanding the Robustness of Graph Neural Networks against Adversarial Attacks	Jun 20, 2024	Adversarial Robustness	CodeCode Available	0
Exploring Layerwise Adversarial Robustness Through the Lens of t-SNE	Jun 20, 2024	Adversarial Robustness	—Unverified	0
Dissecting Adversarial Robustness of Multimodal LM Agents	Jun 18, 2024	Adversarial RobustnessAdversarial Text	CodeCode Available	2
Adversaries With Incentives: A Strategic Alternative to Adversarial Robustness	Jun 17, 2024	Adversarial RobustnessInductive Bias	CodeCode Available	0
Harmonizing Feature Maps: A Graph Convolutional Approach for Enhancing Adversarial Robustness	Jun 17, 2024	Adversarial Robustness	—Unverified	0
Improving Adversarial Robustness via Decoupled Visual Representation Masking	Jun 16, 2024	Adversarial DefenseAdversarial Robustness	CodeCode Available	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