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 1426–1450 of 1746 papers

Title	Date	Tasks	Status	Hype
End-to-end Kernel Learning via Generative Random Fourier Features	Sep 10, 2020	Adversarial Robustness	CodeCode Available	0
Second Order Optimization for Adversarial Robustness and Interpretability	Sep 10, 2020	Adversarial Robustness	—Unverified	0
Dual Manifold Adversarial Robustness: Defense against Lp and non-Lp Adversarial Attacks	Sep 5, 2020	Adversarial Robustness	—Unverified	0
Adversarially Robust Neural Architectures	Sep 2, 2020	Adversarial AttackAdversarial Robustness	—Unverified	0
Perceptual Deep Neural Networks: Adversarial Robustness through Input Recreation	Sep 2, 2020	Adversarial RobustnessSuper-Resolution	—Unverified	0
Likelihood Landscapes: A Unifying Principle Behind Many Adversarial Defenses	Aug 25, 2020	Adversarial DefenseAdversarial Robustness	—Unverified	0
Rethinking Non-idealities in Memristive Crossbars for Adversarial Robustness in Neural Networks	Aug 25, 2020	Adversarial Robustness	—Unverified	0
Towards adversarial robustness with 01 loss neural networks	Aug 20, 2020	Adversarial RobustnessBinary Classification	CodeCode Available	0
Improving adversarial robustness of deep neural networks by using semantic information	Aug 18, 2020	Adversarial AttackAdversarial Robustness	—Unverified	0
Adversarial Attack and Defense Strategies for Deep Speaker Recognition Systems	Aug 18, 2020	Adversarial AttackAdversarial Robustness	CodeCode Available	1
A Deep Dive into Adversarial Robustness in Zero-Shot Learning	Aug 17, 2020	Adversarial RobustnessBIG-bench Machine Learning	CodeCode Available	0
Adversarial Concurrent Training: Optimizing Robustness and Accuracy Trade-off of Deep Neural Networks	Aug 16, 2020	Adversarial Robustness	CodeCode Available	0
On the Generalization Properties of Adversarial Training	Aug 15, 2020	Adversarial Robustness	—Unverified	0
Adversary Agnostic Robust Deep Reinforcement Learning	Aug 14, 2020	Adversarial RobustnessAtari Games	—Unverified	0
Feature Binding with Category-Dependant MixUp for Semantic Segmentation and Adversarial Robustness	Aug 13, 2020	Adversarial RobustnessDenoising	—Unverified	0
Improve Generalization and Robustness of Neural Networks via Weight Scale Shifting Invariant Regularizations	Aug 7, 2020	Adversarial Robustness	—Unverified	0
TREND: Transferability based Robust ENsemble Design	Aug 4, 2020	Adversarial RobustnessQuantization	CodeCode Available	0
Adversarial Robustness for Machine Learning Cyber Defenses Using Log Data	Jul 29, 2020	Adversarial AttackAdversarial Robustness	—Unverified	0
From Sound Representation to Model Robustness	Jul 27, 2020	Adversarial AttackAdversarial Robustness	—Unverified	0
Robust Collective Classification against Structural Attacks	Jul 26, 2020	Adversarial RobustnessClassification	—Unverified	0
Hierarchical Verification for Adversarial Robustness	Jul 23, 2020	Adversarial Robustness	—Unverified	0
Neural Networks with Recurrent Generative Feedback	Jul 17, 2020	Adversarial Robustness	CodeCode Available	1
Certifiably Adversarially Robust Detection of Out-of-Distribution Data	Jul 16, 2020	Adversarial RobustnessOut of Distribution (OOD) Detection	CodeCode Available	1
On Adversarial Robustness: A Neural Architecture Search perspective	Jul 16, 2020	Adversarial RobustnessNeural Architecture Search	CodeCode Available	0
Failure Modes of Variational Autoencoders and Their Effects on Downstream Tasks	Jul 14, 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