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 1281–1290 of 1808 papers

Title	Date	Tasks	Status	Hype
SurFree: a fast surrogate-free black-box attack	Nov 25, 2020	Adversarial Attack	CodeCode Available	1
Augmented Lagrangian Adversarial Attacks	Nov 24, 2020	Adversarial AttackComputational Efficiency	CodeCode Available	1
A Sweet Rabbit Hole by DARCY: Using Honeypots to Detect Universal Trigger's Adversarial Attacks	Nov 20, 2020	Adversarial Attack	—Unverified	0
Multi-Task Adversarial Attack	Nov 19, 2020	Adversarial Attack	—Unverified	0
Adversarial Profiles: Detecting Out-Distribution & Adversarial Samples in Pre-trained CNNs	Nov 18, 2020	Adversarial Attack	—Unverified	0
FoolHD: Fooling speaker identification by Highly imperceptible adversarial Disturbances	Nov 17, 2020	Adversarial AttackSpeaker Identification	CodeCode Available	1
Combining GANs and AutoEncoders for Efficient Anomaly Detection	Nov 16, 2020	Adversarial AttackAnomaly Detection	CodeCode Available	1
Dynamic backdoor attacks against federated learning	Nov 15, 2020	Adversarial AttackBIG-bench Machine Learning	—Unverified	0
Fooling the primate brain with minimal, targeted image manipulation	Nov 11, 2020	Adversarial AttackImage Manipulation	—Unverified	0
Efficient and Transferable Adversarial Examples from Bayesian Neural Networks	Nov 10, 2020	Adversarial AttackBayesian Inference	CodeCode Available	0

Show:10 25 50

← PrevPage 129 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