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 231–240 of 1808 papers

Title	Date	Tasks	Status	Hype
An integrated Auto Encoder-Block Switching defense approach to prevent adversarial attacks	Mar 11, 2022	Adversarial AttackBIG-bench Machine Learning	CodeCode Available	1
Adversarial Vulnerability of Randomized Ensembles	Jun 14, 2022	Adversarial AttackAdversarial Robustness	CodeCode Available	1
An Analysis of Recent Advances in Deepfake Image Detection in an Evolving Threat Landscape	Apr 24, 2024	Adversarial AttackFace Swapping	CodeCode Available	1
PG-Attack: A Precision-Guided Adversarial Attack Framework Against Vision Foundation Models for Autonomous Driving	Jul 18, 2024	Adversarial AttackAutonomous Driving	CodeCode Available	1
AdvDiff: Generating Unrestricted Adversarial Examples using Diffusion Models	Jul 24, 2023	Adversarial AttackAdversarial Defense	CodeCode Available	1
Physical Adversarial Attack meets Computer Vision: A Decade Survey	Sep 30, 2022	Adversarial AttackMedical Diagnosis	CodeCode Available	1
Adversarial Attacks and Detection in Visual Place Recognition for Safer Robot Navigation	Jun 19, 2025	Adversarial AttackRobot Navigation	CodeCode Available	1
AdvFlow: Inconspicuous Black-box Adversarial Attacks using Normalizing Flows	Jul 15, 2020	Adversarial Attack	CodeCode Available	1
Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers	Jun 9, 2019	Adversarial AttackAdversarial Defense	CodeCode Available	1
An Efficient Adversarial Attack for Tree Ensembles	Oct 22, 2020	Adversarial Attackvalid	CodeCode Available	1

Show:10 25 50

← PrevPage 24 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