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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 1361–1370 of 1808 papers

Title	Date	Tasks	Status	Hype
Enhancing Neural Models with Vulnerability via Adversarial Attack	Dec 1, 2020	Adversarial AttackSentence	CodeCode Available	0
Just One Moment: Structural Vulnerability of Deep Action Recognition against One Frame Attack	Nov 30, 2020	Action RecognitionAdversarial Attack	—Unverified	0
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
Adversarial Attack on Facial Recognition using Visible Light	Nov 25, 2020	Adversarial Attackobject-detection	—Unverified	0
Probing Model Signal-Awareness via Prediction-Preserving Input Minimization	Nov 25, 2020	Adversarial AttackVulnerability Detection	—Unverified	0
A Sweet Rabbit Hole by DARCY: Using Honeypots to Detect Universal Trigger's Adversarial Attacks	Nov 20, 2020	Adversarial Attack	—Unverified	0
Multi-Task Adversarial Attack	Nov 19, 2020	Adversarial Attack	—Unverified	0
Adversarial Profiles: Detecting Out-Distribution & Adversarial Samples in Pre-trained CNNs	Nov 18, 2020	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