SOTAVerified|Agents Browse Leaderboard About

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 801–810 of 1808 papers

Title	Date	Tasks	Status	Hype
Defense Against Explanation Manipulation	Nov 8, 2021	Adversarial AttackBIG-bench Machine Learning	—Unverified	0
FlippedRAG: Black-Box Opinion Manipulation Adversarial Attacks to Retrieval-Augmented Generation Models	Jan 6, 2025	Adversarial AttackHallucination	—Unverified	0
Defense against Adversarial Cloud Attack on Remote Sensing Salient Object Detection	Jun 30, 2023	Adversarial AttackDeep Learning	—Unverified	0
Adversarial Music: Real World Audio Adversary Against Wake-word Detection System	Oct 31, 2019	Adversarial AttackReal-World Adversarial Attack	—Unverified	0
Analysis of the vulnerability of machine learning regression models to adversarial attacks using data from 5G wireless networks	May 1, 2025	Adversarial Attackregression	—Unverified	0
An AI-Enabled Framework to Defend Ingenious MDT-based Attacks on the Emerging Zero Touch Cellular Networks	Aug 5, 2023	Adversarial Attack	—Unverified	0
Adversarial Attack and Defense for LoRa Device Identification and Authentication via Deep Learning	Dec 30, 2024	Adversarial AttackDensity Estimation	—Unverified	0
Fooling Adversarial Training with Inducing Noise	Nov 19, 2021	Adversarial Attack	—Unverified	0
Fooling Adversarial Training with Induction Noise	Sep 29, 2021	Adversarial Attack	—Unverified	0
GradMDM: Adversarial Attack on Dynamic Networks	Apr 1, 2023	Adversarial AttackDynamic neural networks	—Unverified	0

Show:10 25 50

← PrevPage 81 of 181Next →

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