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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 1571–1580 of 1808 papers

Title	Date	Tasks	Status	Hype
Wasserstein Smoothing: Certified Robustness against Wasserstein Adversarial Attacks	Oct 23, 2019	Adversarial Attackimage-classification	—Unverified	0
Learning to Learn by Zeroth-Order Oracle	Oct 21, 2019	Adversarial Attack	CodeCode Available	0
Improving Sequence Modeling Ability of Recurrent Neural Networks via Sememes	Oct 20, 2019	Adversarial AttackLanguage Modeling	CodeCode Available	0
SPARK: Spatial-aware Online Incremental Attack Against Visual Tracking	Oct 19, 2019	Adversarial AttackObject Tracking	CodeCode Available	0
LanCe: A Comprehensive and Lightweight CNN Defense Methodology against Physical Adversarial Attacks on Embedded Multimedia Applications	Oct 17, 2019	Adversarial Attack	—Unverified	0
ODE guided Neural Data Augmentation Techniques for Time Series Data and its Benefits on Robustness	Oct 15, 2019	Adversarial AttackData Augmentation	—Unverified	0
Real-world adversarial attack on MTCNN face detection system	Oct 14, 2019	Adversarial Attack	CodeCode Available	0
On Robustness of Neural Ordinary Differential Equations	Oct 12, 2019	Adversarial Attack	CodeCode Available	0
Learning deep forest with multi-scale Local Binary Pattern features for face anti-spoofing	Oct 9, 2019	Adversarial AttackFace Anti-Spoofing	—Unverified	0
Adversarial Learning of Deepfakes in Accounting	Oct 9, 2019	Adversarial AttackERP	—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