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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 1271–1280 of 1808 papers

Title	Date	Tasks	Status	Hype
Contrastive Zero-Shot Learning for Cross-Domain Slot Filling with Adversarial Attack	Dec 1, 2020	Adversarial Attackslot-filling	—Unverified	0
Adversarial Attacks on Deep Graph Matching	Dec 1, 2020	Adversarial AttackDensity Estimation	—Unverified	0
Adversarial Learning for Robust Deep Clustering	Dec 1, 2020	Adversarial AttackClustering	CodeCode Available	1
Just One Moment: Structural Vulnerability of Deep Action Recognition against One Frame Attack	Nov 30, 2020	Action RecognitionAdversarial Attack	—Unverified	0
Guided Adversarial Attack for Evaluating and Enhancing Adversarial Defenses	Nov 30, 2020	Adversarial AttackAdversarial Defense	CodeCode Available	1
A Targeted Universal Attack on Graph Convolutional Network	Nov 29, 2020	Adversarial AttackGraph Neural Network	CodeCode Available	0
FaceGuard: A Self-Supervised Defense Against Adversarial Face Images	Nov 28, 2020	Adversarial AttackAdversarial Defense	—Unverified	0
NaturalAE: Natural and Robust Physical Adversarial Examples for Object Detectors	Nov 27, 2020	Adversarial Attackobject-detection	—Unverified	0
Probing Model Signal-Awareness via Prediction-Preserving Input Minimization	Nov 25, 2020	Adversarial AttackVulnerability Detection	—Unverified	0
Adversarial Attack on Facial Recognition using Visible Light	Nov 25, 2020	Adversarial Attackobject-detection	—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