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 1501–1525 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
How to compare adversarial robustness of classifiers from a global perspective	Apr 22, 2020	Adversarial RobustnessBIG-bench Machine Learning	CodeCode Available	0
Certifying Joint Adversarial Robustness for Model Ensembles	Apr 21, 2020	Adversarial Robustnessmodel	CodeCode Available	0
Adversarial Attack on Deep Learning-Based Splice Localization	Apr 17, 2020	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Adversarial Robustness Guarantees for Random Deep Neural Networks	Apr 13, 2020	Adversarial RobustnessGaussian Processes	CodeCode Available	0
Adversarially Robust Streaming Algorithms via Differential Privacy	Apr 13, 2020	Adversarial Robustness	—Unverified	0
Adversarial Weight Perturbation Helps Robust Generalization	Apr 13, 2020	Adversarial Robustness	CodeCode Available	1
Certifiable Robustness to Adversarial State Uncertainty in Deep Reinforcement Learning	Apr 11, 2020	Adversarial RobustnessCollision Avoidance	—Unverified	0
Approximate Manifold Defense Against Multiple Adversarial Perturbations	Apr 5, 2020	Adversarial Robustnessimage-classification	CodeCode Available	0
SOAR: Second-Order Adversarial Regularization	Apr 4, 2020	Adversarial Robustness	—Unverified	0
Towards Achieving Adversarial Robustness by Enforcing Feature Consistency Across Bit Planes	Apr 1, 2020	Adversarial Robustness	CodeCode Available	1
Towards Deep Learning Models Resistant to Large Perturbations	Mar 30, 2020	Adversarial RobustnessDeep Learning	CodeCode Available	0
Improving out-of-distribution generalization via multi-task self-supervised pretraining	Mar 30, 2020	Adversarial RobustnessDomain Generalization	—Unverified	0
Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning	Mar 28, 2020	Adversarial Robustness	CodeCode Available	1
Challenging the adversarial robustness of DNNs based on error-correcting output codes	Mar 26, 2020	Adversarial AttackAdversarial Robustness	—Unverified	0
Defense Through Diverse Directions	Mar 24, 2020	Adversarial 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