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 831–840 of 1808 papers

Title	Date	Tasks	Status	Hype
Hiding Backdoors within Event Sequence Data via Poisoning Attacks	Aug 20, 2023	Adversarial Attack	—Unverified	0
An Adversarial Attack Defending System for Securing In-Vehicle Networks	Aug 25, 2020	Adversarial Attack	—Unverified	0
Post-train Black-box Defense via Bayesian Boundary Correction	Jun 29, 2023	Activity RecognitionAdversarial Attack	—Unverified	0
Adversarial Attack Type I: Cheat Classifiers by Significant Changes	Sep 3, 2018	Adversarial AttackVocal Bursts Type Prediction	—Unverified	0
A Computationally Efficient Method for Defending Adversarial Deep Learning Attacks	Jun 13, 2019	Adversarial AttackDeep Learning	—Unverified	0
HGAttack: Transferable Heterogeneous Graph Adversarial Attack	Jan 18, 2024	Adversarial Attack	—Unverified	0
Defending Against Adversarial Examples by Regularized Deep Embedding	Sep 25, 2019	Adversarial AttackAdversarial Robustness	—Unverified	0
BB-Patch: BlackBox Adversarial Patch-Attack using Zeroth-Order Optimization	May 9, 2024	Adversarial AttackDeep Learning	—Unverified	0
Defending against Adversarial Attack towards Deep Neural Networks via Collaborative Multi-task Training	Mar 14, 2018	Adversarial Attack	—Unverified	0
Hijacking Vision-and-Language Navigation Agents with Adversarial Environmental Attacks	Dec 3, 2024	Adversarial AttackVision and Language Navigation	—Unverified	0

Show:10 25 50

← PrevPage 84 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