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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 1721–1730 of 1808 papers

Title	Date	Tasks	Status	Hype
Attention, Please! Adversarial Defense via Activation Rectification and Preservation	Nov 24, 2018	Adversarial AttackAdversarial Defense	—Unverified	0
Parametric Noise Injection: Trainable Randomness to Improve Deep Neural Network Robustness against Adversarial Attack	Nov 22, 2018	Adversarial AttackAdversarial Defense	CodeCode Available	0
Task-generalizable Adversarial Attack based on Perceptual Metric	Nov 22, 2018	Adversarial Attackobject-detection	CodeCode Available	0
Intermediate Level Adversarial Attack for Enhanced Transferability	Nov 20, 2018	Adversarial Attack	—Unverified	0
Injecting and removing malignant features in mammography with CycleGAN: Investigation of an automated adversarial attack using neural networks	Nov 19, 2018	Adversarial AttackGenerative Adversarial Network	CodeCode Available	0
Optimal Transport Classifier: Defending Against Adversarial Attacks by Regularized Deep Embedding	Nov 19, 2018	Adversarial AttackGeneral Classification	—Unverified	0
CAAD 2018: Iterative Ensemble Adversarial Attack	Nov 7, 2018	Adversarial Attack	—Unverified	0
FAdeML: Understanding the Impact of Pre-Processing Noise Filtering on Adversarial Machine Learning	Nov 4, 2018	Adversarial AttackBIG-bench Machine Learning	—Unverified	0
Learning to Defend by Learning to Attack	Nov 3, 2018	Adversarial AttackAdversarial Defense	—Unverified	0
Unauthorized AI cannot Recognize Me: Reversible Adversarial Example	Nov 1, 2018	Adversarial AttackBIG-bench Machine Learning	—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