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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 821–830 of 1808 papers

Title	Date	Tasks	Status	Hype
Headless Horseman: Adversarial Attacks on Transfer Learning Models	Apr 20, 2020	Adversarial AttackTransfer Learning	—Unverified	0
Frequency-Tuned Universal Adversarial Attacks	Mar 11, 2020	Adversarial Attack	—Unverified	0
An Adversarial Attack Defending System for Securing In-Vehicle Networks	Aug 25, 2020	Adversarial Attack	—Unverified	0
Post-train Black-box Defense via Bayesian Boundary Correction	Jun 29, 2023	Activity RecognitionAdversarial Attack	—Unverified	0
Adversarial Attack Type I: Cheat Classifiers by Significant Changes	Sep 3, 2018	Adversarial AttackVocal Bursts Type Prediction	—Unverified	0
From Sound Representation to Model Robustness	Jul 27, 2020	Adversarial AttackAdversarial Robustness	—Unverified	0
Hear No Evil: Towards Adversarial Robustness of Automatic Speech Recognition via Multi-Task Learning	Apr 5, 2022	Adversarial AttackAdversarial Robustness	—Unverified	0
GAIM: Attacking Graph Neural Networks via Adversarial Influence Maximization	Aug 20, 2024	Adversarial AttackGraph Neural Network	—Unverified	0
GasHis-Transformer: A Multi-scale Visual Transformer Approach for Gastric Histopathological Image Detection	Apr 29, 2021	Adversarial AttackGeneral Classification	—Unverified	0
A Computationally Efficient Method for Defending Adversarial Deep Learning Attacks	Jun 13, 2019	Adversarial AttackDeep Learning	—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