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Adversarial Attack

An Adversarial Attack is a technique to find a perturbation that changes the prediction of a machine learning model. The perturbation can be very small and imperceptible to human eyes.

Source: Recurrent Attention Model with Log-Polar Mapping is Robust against Adversarial Attacks

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1641–1650 of 1808 papers

Title	Date	Tasks	Status	Hype
ShieldNets: Defending Against Adversarial Attacks Using Probabilistic Adversarial Robustness	Jun 1, 2019	Adversarial AttackAdversarial Robustness	—Unverified	0
Decoupling Direction and Norm for Efficient Gradient-Based L2 Adversarial Attacks and Defenses	Jun 1, 2019	Adversarial AttackAdversarial Defense	CodeCode Available	0
Improving VAEs' Robustness to Adversarial Attack	Jun 1, 2019	Adversarial Attack	—Unverified	0
Enhancing Transformation-based Defenses using a Distribution Classifier	Jun 1, 2019	Adversarial Attack	—Unverified	0
Feature Space Perturbations Yield More Transferable Adversarial Examples	Jun 1, 2019	Adversarial Attack	CodeCode Available	0
Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness	May 31, 2019	Adversarial AttackAdversarial Attack Detection	CodeCode Available	0
Real-Time Adversarial Attacks	May 31, 2019	Adversarial AttackBIG-bench Machine Learning	CodeCode Available	0
Bandlimiting Neural Networks Against Adversarial Attacks	May 30, 2019	Adversarial Attack	—Unverified	0
Identifying Classes Susceptible to Adversarial Attacks	May 30, 2019	Adversarial Attack	—Unverified	0
Robust Sparse Regularization: Simultaneously Optimizing Neural Network Robustness and Compactness	May 30, 2019	Adversarial Attack	—Unverified	0

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All datasets CIFAR-10 CIFAR-100

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	Xu et al.	Attack: PGD20	78.68	—	Unverified
2	3-ensemble of multi-resolution self-ensembles	Attack: AutoAttack	78.13	—	Unverified
3	TRADES-ANCRA/ResNet18	Attack: AutoAttack	59.7	—	Unverified
4	AdvTraining [madry2018]	Attack: PGD20	48.44	—	Unverified
5	TRADES [zhang2019b]	Attack: PGD20	45.9	—	Unverified
6	XU-Net	Robust Accuracy	1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	3-ensemble of multi-resolution self-ensembles	Attack: AutoAttack	51.28	—	Unverified
2	multi-resolution self-ensembles	Attack: AutoAttack	47.85	—	Unverified