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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 1591–1600 of 1808 papers

Title	Date	Tasks	Status	Hype
Adversarial Attack on Skeleton-based Human Action Recognition	Sep 14, 2019	Action RecognitionAdversarial Attack	—Unverified	0
An Empirical Investigation of Randomized Defenses against Adversarial Attacks	Sep 12, 2019	Adversarial AttackGeneral Classification	CodeCode Available	0
BOSH: An Efficient Meta Algorithm for Decision-based Attacks	Sep 10, 2019	Adversarial AttackBayesian Optimization	—Unverified	0
Localized Adversarial Training for Increased Accuracy and Robustness in Image Classification	Sep 10, 2019	Adversarial AttackGeneral Classification	—Unverified	0
FDA: Feature Disruptive Attack	Sep 10, 2019	Adversarial Attackimage-classification	CodeCode Available	0
STA: Adversarial Attacks on Siamese Trackers	Sep 8, 2019	Adversarial Attack	—Unverified	0
Targeted Mismatch Adversarial Attack: Query with a Flower to Retrieve the Tower	Aug 24, 2019	Adversarial AttackRetrieval	CodeCode Available	0
AdvHat: Real-world adversarial attack on ArcFace Face ID system	Aug 23, 2019	Adversarial AttackReal-World Adversarial Attack	CodeCode Available	0
Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks	Aug 17, 2019	Adversarial Attack	CodeCode Available	1
DAPAS : Denoising Autoencoder to Prevent Adversarial attack in Semantic Segmentation	Aug 14, 2019	Adversarial AttackDenoising	—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