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 971–980 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
Internal Wasserstein Distance for Adversarial Attack and Defense	Mar 13, 2021	Adversarial AttackAdversarial Defense	—Unverified	0	0
Interpolation between CNNs and ResNets	Jan 1, 2020	Adversarial Attackimage-classification	—Unverified	0	0
A Framework for Adversarial Analysis of Decision Support Systems Prior to Deployment	May 27, 2025	Adversarial AttackAdversarial Defense	—Unverified	0	0
Interpreting and Evaluating Neural Network Robustness	May 10, 2019	Adversarial Attack	—Unverified	0	0
Interpreting Hidden Semantics in the Intermediate Layers of 3D Point Cloud Classification Neural Network	Mar 12, 2023	3D Point Cloud ClassificationAdversarial Attack	—Unverified	0	0
Discovering Failure Modes of Text-guided Diffusion Models via Adversarial Search	Jun 1, 2023	Adversarial AttackEfficient Exploration	—Unverified	0	0
The Efficacy of SHIELD under Different Threat Models	Feb 1, 2019	Adversarial Attackimage-classification	—Unverified	0	0
MF-CLIP: Leveraging CLIP as Surrogate Models for No-box Adversarial Attacks	Jul 13, 2023	Adversarial AttackAttribute	—Unverified	0	0
Adversarial Attacks on Time-Series Intrusion Detection for Industrial Control Systems	Nov 8, 2019	Adversarial AttackIntrusion Detection	—Unverified	0	0
Exploring the Robustness of NMT Systems to Nonsensical Inputs	Aug 3, 2019	Adversarial AttackDecoder	—Unverified	0	0

Show:10 25 50

← PrevPage 98 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