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 1701–1710 of 1808 papers

TitleStatusHype
Adversarial Metric Attack and Defense for Person Re-identificationCode0
Strong Black-box Adversarial Attacks on Unsupervised Machine Learning Models—0
Weighted-Sampling Audio Adversarial Example Attack—0
Hessian-Aware Zeroth-Order Optimization for Black-Box Adversarial Attack—0
Seeing isn't Believing: Practical Adversarial Attack Against Object Detectors—0
Adversarial Attack and Defense on Graph Data: A SurveyCode0
A Survey of Safety and Trustworthiness of Deep Neural Networks: Verification, Testing, Adversarial Attack and Defence, and Interpretability—0
Trust Region Based Adversarial Attack on Neural NetworksCode0
Learning Transferable Adversarial Examples via Ghost NetworksCode0
Deep-RBF Networks Revisited: Robust Classification with Rejection—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