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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 1741–1750 of 1808 papers

Title	Date	Tasks	Status	Hype
Adversarial Defense via Data Dependent Activation Function and Total Variation Minimization	Sep 23, 2018	Adversarial AttackAdversarial Defense	CodeCode Available	0
Efficient Formal Safety Analysis of Neural Networks	Sep 19, 2018	Adversarial AttackAdversarial Defense	CodeCode Available	0
Query-Efficient Black-Box Attack by Active Learning	Sep 13, 2018	Active LearningAdversarial Attack	—Unverified	0
Isolated and Ensemble Audio Preprocessing Methods for Detecting Adversarial Examples against Automatic Speech Recognition	Sep 11, 2018	Adversarial AttackAutomatic Speech Recognition	—Unverified	0
Certified Adversarial Robustness with Additive Noise	Sep 10, 2018	Adversarial AttackAdversarial Robustness	CodeCode Available	0
Query Attack via Opposite-Direction Feature:Towards Robust Image Retrieval	Sep 7, 2018	Adversarial AttackGeneral Classification	CodeCode Available	0
IDSGAN: Generative Adversarial Networks for Attack Generation against Intrusion Detection	Sep 6, 2018	Adversarial AttackIntrusion Detection	—Unverified	0
Adversarial Attack Type I: Cheat Classifiers by Significant Changes	Sep 3, 2018	Adversarial AttackVocal Bursts Type Prediction	—Unverified	0
Maximal Jacobian-based Saliency Map Attack	Aug 23, 2018	Adversarial AttackClassification	—Unverified	0
Stochastic Combinatorial Ensembles for Defending Against Adversarial Examples	Aug 20, 2018	Adversarial AttackMetric 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