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 1231–1240 of 1808 papers

TitleStatusHype
Exploring Adversarial Fake Images on Face Manifold—0
Adversarial Attack Attribution: Discovering Attributable Signals in Adversarial ML Attacks—0
Robust Text CAPTCHAs Using Adversarial Examples—0
Local Competition and Stochasticity for Adversarial Robustness in Deep Learning—0
Towards Robustness of Deep Neural Networks via Regularization—0
Meta-Attack: Class-Agnostic and Model-Agnostic Physical Adversarial Attack—0
Adversarial Attack on Deep Cross-Modal Hamming Retrieval—0
Consistency-Sensitivity Guided Ensemble Black-Box Adversarial Attacks in Low-Dimensional Spaces—0
Adversarial Example Detection Using Latent Neighborhood Graph—0
Stabilized Medical Attacks—0
Show:102550
← PrevPage 124 of 181Next →
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