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 651–660 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
Disttack: Graph Adversarial Attacks Toward Distributed GNN Training	May 10, 2024	Adversarial AttackGraph Learning	CodeCode Available	0	5
Logit Margin Matters: Improving Transferable Targeted Adversarial Attack by Logit Calibration	Mar 7, 2023	Adversarial Attack	CodeCode Available	0	5
Any Target Can be Offense: Adversarial Example Generation via Generalized Latent Infection	Jul 17, 2024	Adversarial Attack	CodeCode Available	0	5
Look Closer to Your Enemy: Learning to Attack via Teacher-Student Mimicking	Jul 27, 2022	Adversarial AttackDomain Adaptation	CodeCode Available	0	5
Controversial stimuli: pitting neural networks against each other as models of human recognition	Nov 21, 2019	Adversarial Attack	CodeCode Available	0	5
AICAttack: Adversarial Image Captioning Attack with Attention-Based Optimization	Feb 19, 2024	Adversarial AttackImage Captioning	CodeCode Available	0	5
Expanding Scope: Adapting English Adversarial Attacks to Chinese	Jun 8, 2023	Adversarial AttackAdversarial Robustness	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
Accelerating Monte Carlo Bayesian Inference via Approximating Predictive Uncertainty over Simplex	May 29, 2019	Adversarial AttackBayesian Inference	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

Show:10 25 50

← PrevPage 66 of 181Next →

All datasets CIFAR-10 CIFAR-100

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	ResNet20	Test Accuracy	89.95	89.95(1)	Community Verified
2	Xu et al.	Attack: PGD20	78.68	—	Unverified
3	3-ensemble of multi-resolution self-ensembles	Attack: AutoAttack	78.13	—	Unverified
4	TRADES-ANCRA/ResNet18	Attack: AutoAttack	59.7	—	Unverified
5	AdvTraining [madry2018]	Attack: PGD20	48.44	—	Unverified
6	TRADES [zhang2019b]	Attack: PGD20	45.9	—	Unverified
7	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