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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 1771–1780 of 1808 papers

Title	Date	Tasks	Status	Hype
Learn To Pay Attention	Apr 6, 2018	Adversarial AttackGeneral Classification	CodeCode Available	0
Protection against Cloning for Deep Learning	Mar 29, 2018	Adversarial AttackDeep Learning	—Unverified	0
Adversarial Defense based on Structure-to-Signal Autoencoders	Mar 21, 2018	Adversarial AttackAdversarial Defense	—Unverified	0
DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems	Mar 20, 2018	Adversarial AttackDeep Learning	—Unverified	0
Improving Transferability of Adversarial Examples with Input Diversity	Mar 19, 2018	Adversarial AttackDiversity	CodeCode Available	0
Vulnerability of Deep Learning	Mar 16, 2018	Adversarial AttackDeep Learning	—Unverified	0
Defending against Adversarial Attack towards Deep Neural Networks via Collaborative Multi-task Training	Mar 14, 2018	Adversarial Attack	—Unverified	0
ASP:A Fast Adversarial Attack Example Generation Framework based on Adversarial Saliency Prediction	Feb 15, 2018	Adversarial Attackimage-classification	—Unverified	0
Security Analysis and Enhancement of Model Compressed Deep Learning Systems under Adversarial Attacks	Feb 14, 2018	Adversarial Attack	—Unverified	0
Blind Pre-Processing: A Robust Defense Method Against Adversarial Examples	Feb 5, 2018	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