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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 771–780 of 1808 papers

Title	Date	Tasks	Status	Hype
Order-Disorder: Imitation Adversarial Attacks for Black-box Neural Ranking Models	Sep 14, 2022	Adversarial AttackLanguage Modelling	CodeCode Available	1
ADMM based Distributed State Observer Design under Sparse Sensor Attacks	Sep 13, 2022	Adversarial Attack	—Unverified	0
PINCH: An Adversarial Extraction Attack Framework for Deep Learning Models	Sep 13, 2022	Adversarial AttackDeep Learning	—Unverified	0
Sample Complexity of an Adversarial Attack on UCB-based Best-arm Identification Policy	Sep 13, 2022	Adversarial Attack	—Unverified	0
Generate synthetic samples from tabular data	Sep 12, 2022	Adversarial Attack	CodeCode Available	0
Resisting Deep Learning Models Against Adversarial Attack Transferability via Feature Randomization	Sep 11, 2022	Adversarial AttackAutonomous Navigation	CodeCode Available	0
Scattering Model Guided Adversarial Examples for SAR Target Recognition: Attack and Defense	Sep 11, 2022	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Attacking the Spike: On the Transferability and Security of Spiking Neural Networks to Adversarial Examples	Sep 7, 2022	Adversarial Attack	—Unverified	0
Impact of Scaled Image on Robustness of Deep Neural Networks	Sep 2, 2022	Adversarial Attackimage-classification	—Unverified	0
A Black-Box Attack on Optical Character Recognition Systems	Aug 30, 2022	Adversarial AttackBinarization	—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