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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 481–490 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
Exact Adversarial Attack to Image Captioning via Structured Output Learning with Latent Variables	May 10, 2019	Adversarial AttackImage Captioning	CodeCode Available	0	5
AdvGPS: Adversarial GPS for Multi-Agent Perception Attack	Jan 30, 2024	Adversarial Attackobject-detection	CodeCode Available	0	5
Evaluating the Validity of Word-level Adversarial Attacks with Large Language Models	Aug 15, 2024	Adversarial AttackLanguage Modeling	CodeCode Available	0	5
EvoBA: An Evolution Strategy as a Strong Baseline forBlack-Box Adversarial Attacks	Jul 12, 2021	Adversarial Attack	CodeCode Available	0	5
Network transferability of adversarial patches in real-time object detection	Aug 28, 2024	Adversarial AttackObject	CodeCode Available	0	5
Neural Fingerprints for Adversarial Attack Detection	Nov 7, 2024	Adversarial AttackAdversarial Attack Detection	CodeCode Available	0	5
Excess Capacity and Backdoor Poisoning	Sep 2, 2021	Adversarial AttackBackdoor Attack	CodeCode Available	0	5
Army of Thieves: Enhancing Black-Box Model Extraction via Ensemble based sample selection	Nov 8, 2023	Active LearningAdversarial Attack	CodeCode Available	0	5
Noise-based cyberattacks generating fake P300 waves in brain–computer interfaces	Jul 10, 2021	Adversarial AttackCyber Attack Investigation	CodeCode Available	0	5
Evaluating and Understanding the Robustness of Adversarial Logit Pairing	Jul 26, 2018	Adversarial Attack	CodeCode Available	0	5

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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