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 1141–1150 of 1808 papers

TitleStatusHype
NoisyHate: Mining Online Human-Written Perturbations for Realistic Robustness Benchmarking of Content Moderation Models—0
A Differentiable Language Model Adversarial Attack on Text Classifiers—0
Non-Asymptotic Bounds for Adversarial Excess Risk under Misspecified Models—0
Nonconvex Zeroth-Order Stochastic ADMM Methods with Lower Function Query Complexity—0
No Query, No Access—0
No Surprises: Training Robust Lung Nodule Detection for Low-Dose CT Scans by Augmenting with Adversarial Attacks—0
Fooling Network Interpretation in Image Classification—0
An alternative proof of the vulnerability of retrieval in high intrinsic dimensionality neighborhood—0
Not So Robust After All: Evaluating the Robustness of Deep Neural Networks to Unseen Adversarial Attacks—0
Now You See It, Now You Dont: Adversarial Vulnerabilities in Computational Pathology—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