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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 1641–1650 of 1808 papers

Title	Date	Tasks	Status	Hype
High Frequency Component Helps Explain the Generalization of Convolutional Neural Networks	May 28, 2019	Adversarial AttackVocal Bursts Intensity Prediction	CodeCode Available	1
Scaleable input gradient regularization for adversarial robustness	May 27, 2019	Adversarial AttackAdversarial Defense	CodeCode Available	0
Fooling Detection Alone is Not Enough: First Adversarial Attack against Multiple Object Tracking	May 27, 2019	Adversarial AttackAutonomous Driving	CodeCode Available	1
Thwarting finite difference adversarial attacks with output randomization	May 23, 2019	Adversarial Attack	—Unverified	0
DoPa: A Comprehensive CNN Detection Methodology against Physical Adversarial Attacks	May 21, 2019	Adversarial AttackAdversarial Attack Detection	—Unverified	0
Taking Care of The Discretization Problem: A Comprehensive Study of the Discretization Problem and A Black-Box Adversarial Attack in Discrete Integer Domain	May 19, 2019	Adversarial Attack	CodeCode Available	0
A critique of the DeepSec Platform for Security Analysis of Deep Learning Models	May 17, 2019	Adversarial Attack	—Unverified	0
Harnessing the Vulnerability of Latent Layers in Adversarially Trained Models	May 13, 2019	Adversarial Attack	CodeCode Available	0
Exact Adversarial Attack to Image Captioning via Structured Output Learning with Latent Variables	May 10, 2019	Adversarial AttackImage Captioning	CodeCode Available	0
Interpreting and Evaluating Neural Network Robustness	May 10, 2019	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