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 1201–1210 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
Optimal Cost Constrained Adversarial Attacks For Multiple Agent Systems	Nov 1, 2023	Adversarial Attack	—Unverified	0	0
Optimal Transport Classifier: Defending Against Adversarial Attacks by Regularized Deep Embedding	Nov 19, 2018	Adversarial AttackGeneral Classification	—Unverified	0	0
Optimizing Key-Selection for Face-based One-Time Biometrics via Morphing	Oct 4, 2023	Adversarial AttackBlocking	—Unverified	0	0
Adversarially Robust Conformal Prediction	Sep 29, 2021	Adversarial AttackConformal Prediction	—Unverified	0	0
OT-Attack: Enhancing Adversarial Transferability of Vision-Language Models via Optimal Transport Optimization	Dec 7, 2023	Adversarial AttackData Augmentation	—Unverified	0	0
Bridging Optimal Transport and Jacobian Regularization by Optimal Trajectory for Enhanced Adversarial Defense	Mar 21, 2023	Adversarial AttackAdversarial Defense	—Unverified	0	0
A Deep Genetic Programming based Methodology for Art Media Classification Robust to Adversarial Perturbations	Oct 3, 2020	Adversarial AttackDeep Learning	—Unverified	0	0
Adversarially Robust Classification by Conditional Generative Model Inversion	Jan 12, 2022	Adversarial AttackClassification	—Unverified	0	0
Towards Robust and Secure Embodied AI: A Survey on Vulnerabilities and Attacks	Feb 18, 2025	Adversarial AttackAutonomous Vehicles	—Unverified	0	0
Overlap-Aware Feature Learning for Robust Unsupervised Domain Adaptation for 3D Semantic Segmentation	Apr 2, 2025	3D Semantic SegmentationAdversarial Attack	—Unverified	0	0

Show:10 25 50

← PrevPage 121 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