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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 781–790 of 1808 papers

Title	Date	Tasks	Status	Hype
Dynamic ensemble selection based on Deep Neural Network Uncertainty Estimation for Adversarial Robustness	Aug 1, 2023	Adversarial AttackAdversarial Robustness	—Unverified	0
A Novel Deep Learning based Model to Defend Network Intrusion Detection System against Adversarial Attacks	Jul 31, 2023	Adversarial AttackIntrusion Detection	—Unverified	0
Defense of Adversarial Ranking Attack in Text Retrieval: Benchmark and Baseline via Detection	Jul 31, 2023	Adversarial AttackInformation Retrieval	—Unverified	0
On Neural Network approximation of ideal adversarial attack and convergence of adversarial training	Jul 30, 2023	Adversarial Attack	—Unverified	0
When Measures are Unreliable: Imperceptible Adversarial Perturbations toward Top-k Multi-Label Learning	Jul 27, 2023	Adversarial AttackMulti-Label Learning	CodeCode Available	0
On the unreasonable vulnerability of transformers for image restoration -- and an easy fix	Jul 25, 2023	Adversarial AttackAdversarial Robustness	—Unverified	0
Imperceptible Physical Attack against Face Recognition Systems via LED Illumination Modulation	Jul 25, 2023	Adversarial AttackFace Detection	—Unverified	0
Adversarial Attacks on Traffic Sign Recognition: A Survey	Jul 17, 2023	Adversarial AttackAutonomous Vehicles	—Unverified	0
On the Robustness of Split Learning against Adversarial Attacks	Jul 16, 2023	Adversarial Attack	—Unverified	0
RFLA: A Stealthy Reflected Light Adversarial Attack in the Physical World	Jul 14, 2023	Adversarial AttackObject	CodeCode Available	0

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All datasets CIFAR-10 CIFAR-100

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	ResNet20	Test Accuracy	89.95	89.95(1)	Community Verified
2	Xu et al.	Attack: PGD20	78.68	—	Unverified
3	3-ensemble of multi-resolution self-ensembles	Attack: AutoAttack	78.13	—	Unverified
4	TRADES-ANCRA/ResNet18	Attack: AutoAttack	59.7	—	Unverified
5	AdvTraining [madry2018]	Attack: PGD20	48.44	—	Unverified
6	TRADES [zhang2019b]	Attack: PGD20	45.9	—	Unverified
7	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