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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 1761–1770 of 1808 papers

Title	Date	Tasks	Status	Hype
GenAttack: Practical Black-box Attacks with Gradient-Free Optimization	May 28, 2018	Adversarial AttackAdversarial Robustness	CodeCode Available	0
Knowledge Distillation with Adversarial Samples Supporting Decision Boundary	May 15, 2018	Adversarial AttackKnowledge Distillation	CodeCode Available	0
ADef: an Iterative Algorithm to Construct Adversarial Deformations	Apr 20, 2018	Adversarial AttackGeneral Classification	CodeCode Available	0
ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector	Apr 16, 2018	Adversarial AttackAutonomous Vehicles	CodeCode Available	0
An ADMM-Based Universal Framework for Adversarial Attacks on Deep Neural Networks	Apr 9, 2018	Adversarial Attack	—Unverified	0
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

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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