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 201–210 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
Iron Sharpens Iron: Defending Against Attacks in Machine-Generated Text Detection with Adversarial Training	Feb 18, 2025	Adversarial AttackText Detection	CodeCode Available	1	5
Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNs	Oct 6, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
Adversarial Attack on Large Scale Graph	Sep 8, 2020	Adversarial Attack	CodeCode Available	1	5
LGV: Boosting Adversarial Example Transferability from Large Geometric Vicinity	Jul 26, 2022	Adversarial Attack	CodeCode Available	1	5
Can You Spot the Chameleon? Adversarially Camouflaging Images from Co-Salient Object Detection	Sep 19, 2020	Adversarial AttackCo-Salient Object Detection	CodeCode Available	1	5
Malacopula: adversarial automatic speaker verification attacks using a neural-based generalised Hammerstein model	Aug 17, 2024	Adversarial AttackSpeaker Verification	CodeCode Available	1	5
GenoArmory: A Unified Evaluation Framework for Adversarial Attacks on Genomic Foundation Models	May 16, 2025	Adversarial AttackAdversarial Defense	CodeCode Available	1	5
Boosting Adversarial Transferability via Gradient Relevance Attack	Jan 1, 2023	Adversarial Attack	CodeCode Available	1	5
Adversarial Ranking Attack and Defense	Feb 26, 2020	Adversarial AttackImage Retrieval	CodeCode Available	1	5
AdvDiff: Generating Unrestricted Adversarial Examples using Diffusion Models	Jul 24, 2023	Adversarial AttackAdversarial Defense	CodeCode Available	1	5

Show:10 25 50

← PrevPage 21 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