SOTAVerified

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 AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1371–1380 of 1808 papers

TitleStatusHype
Transferable Perturbations of Deep Feature Distributions—0
Revisiting Physical-World Adversarial Attack on Traffic Sign Recognition: A Commercial Systems Perspective—0
Rewriting Meaningful Sentences via Conditional BERT Sampling and an application on fooling text classifiers—0
Transferable Physical Attack against Object Detection with Separable Attention—0
Adversarial Attacks on Speech Recognition Systems for Mission-Critical Applications: A Survey—0
Rigid Body Adversarial Attacks—0
A Black-Box Attack on Code Models via Representation Nearest Neighbor Search—0
ADAGIO: Interactive Experimentation with Adversarial Attack and Defense for Audio—0
Robust Active Learning (RoAL): Countering Dynamic Adversaries in Active Learning with Elastic Weight Consolidation—0
Robust Adversarial Attacks Detection based on Explainable Deep Reinforcement Learning For UAV Guidance and Planning—0
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All datasetsCIFAR-10CIFAR-100

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Xu et al.Attack: PGD2078.68—Unverified
23-ensemble of multi-resolution self-ensemblesAttack: AutoAttack78.13—Unverified
3TRADES-ANCRA/ResNet18Attack: AutoAttack59.7—Unverified
4AdvTraining [madry2018]Attack: PGD2048.44—Unverified
5TRADES [zhang2019b]Attack: PGD2045.9—Unverified
6XU-NetRobust Accuracy1—Unverified
#ModelMetricClaimedVerifiedStatus
13-ensemble of multi-resolution self-ensemblesAttack: AutoAttack51.28—Unverified
2multi-resolution self-ensemblesAttack: AutoAttack47.85—Unverified