SOTAVerified|Agents Browse Leaderboard About Blog

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 1501–1510 of 1746 papers

Title	Date	Tasks	Status	Hype
Efficient Exact Verification of Binarized Neural Networks	May 7, 2020	Adversarial Robustness	CodeCode Available	1
Measuring Adversarial Robustness using a Voronoi-Epsilon Adversary	May 6, 2020	Adversarial Robustness	CodeCode Available	0
Enhancing Intrinsic Adversarial Robustness via Feature Pyramid Decoder	May 6, 2020	Adversarial RobustnessDecoder	CodeCode Available	1
Do Gradient-based Explanations Tell Anything About Adversarial Robustness to Android Malware?	May 4, 2020	Adversarial RobustnessAndroid Malware Detection	—Unverified	0
Biologically inspired sleep algorithm for increased generalization and adversarial robustness in deep neural networks	May 1, 2020	Adversarial RobustnessGeneral Classification	—Unverified	0
Improving Adversarial Robustness Requires Revisiting Misclassified Examples	May 1, 2020	Adversarial Robustness	CodeCode Available	1
Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness	Apr 30, 2020	Adversarial Robustness	CodeCode Available	1
Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks	Apr 28, 2020	Adversarial Robustness	—Unverified	0
Improving the Interpretability of fMRI Decoding using Deep Neural Networks and Adversarial Robustness	Apr 23, 2020	Adversarial Robustness	CodeCode Available	0
QUANOS- Adversarial Noise Sensitivity Driven Hybrid Quantization of Neural Networks	Apr 22, 2020	Adversarial RobustnessQuantization	—Unverified	0

Show:10 25 50

← PrevPage 151 of 175Next →

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