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 1221–1230 of 1808 papers

TitleStatusHype
Fooling Adversarial Training with Inducing Noise—0
Fooling Adversarial Training with Induction Noise—0
FoolSDEdit: Deceptively Steering Your Edits Towards Targeted Attribute-aware Distribution—0
Forbidden Facts: An Investigation of Competing Objectives in Llama-2—0
Fortify Machine Learning Production Systems: Detect and Classify Adversarial Attacks—0
FRAUD-RLA: A new reinforcement learning adversarial attack against credit card fraud detection—0
Frequency-aware GAN for Adversarial Manipulation Generation—0
Frequency-Tuned Universal Adversarial Attacks—0
From Environmental Sound Representation to Robustness of 2D CNN Models Against Adversarial Attacks—0
From Sound Representation to Model Robustness—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