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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 541–550 of 1808 papers

Title	Date	Tasks	Status	Hype
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
AdvDiff: Generating Unrestricted Adversarial Examples using Diffusion Models	Jul 24, 2023	Adversarial AttackAdversarial Defense	CodeCode Available	1
OUTFOX: LLM-Generated Essay Detection Through In-Context Learning with Adversarially Generated Examples	Jul 21, 2023	Adversarial AttackAdversarial Attack Detection	CodeCode Available	1
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
On the Sensitivity of Deep Load Disaggregation to Adversarial Attacks	Jul 14, 2023	Adversarial Attackenergy management	—Unverified	0
RFLA: A Stealthy Reflected Light Adversarial Attack in the Physical World	Jul 14, 2023	Adversarial AttackObject	CodeCode Available	0
Frequency Domain Adversarial Training for Robust Volumetric Medical Segmentation	Jul 14, 2023	Adversarial AttackDeep Learning	CodeCode Available	1
Multi-objective Evolutionary Search of Variable-length Composite Semantic Perturbations	Jul 13, 2023	Adversarial AttackAutoML	—Unverified	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