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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 561–570 of 1808 papers

Title	Date	Tasks	Status	Hype
ASP:A Fast Adversarial Attack Example Generation Framework based on Adversarial Saliency Prediction	Feb 15, 2018	Adversarial Attackimage-classification	—Unverified	0
ADAGIO: Interactive Experimentation with Adversarial Attack and Defense for Audio	May 30, 2018	Adversarial AttackAudio Compression	—Unverified	0
A Simple Framework to Enhance the Adversarial Robustness of Deep Learning-based Intrusion Detection System	Dec 6, 2023	Adversarial AttackAdversarial Robustness	—Unverified	0
As Firm As Their Foundations: Can open-sourced foundation models be used to create adversarial examples for downstream tasks?	Mar 19, 2024	Adversarial AttackImage Captioning	—Unverified	0
Fooling the primate brain with minimal, targeted image manipulation	Nov 11, 2020	Adversarial AttackImage Manipulation	—Unverified	0
A Bayes-Optimal View on Adversarial Examples	Feb 20, 2020	Adversarial Attack	—Unverified	0
DO-AutoEncoder: Learning and Intervening Bivariate Causal Mechanisms in Images	Sep 25, 2019	Adversarial AttackRepresentation Learning	—Unverified	0
DoPa: A Comprehensive CNN Detection Methodology against Physical Adversarial Attacks	May 21, 2019	Adversarial AttackAdversarial Attack Detection	—Unverified	0
AS2T: Arbitrary Source-To-Target Adversarial Attack on Speaker Recognition Systems	Jun 7, 2022	Adversarial AttackSpeaker Recognition	—Unverified	0
Adversarial Identity Injection for Semantic Face Image Synthesis	Apr 16, 2024	Adversarial AttackFace Generation	—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