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 861–870 of 1808 papers

TitleStatusHype
Gradient-guided Unsupervised Text Style Transfer via Contrastive Learning—0
Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense—0
Imperceptible CMOS camera dazzle for adversarial attacks on deep neural networks—0
Defending Against Adversarial Attack in ECG Classification with Adversarial Distillation Training—0
Adversarial Attacks to Machine Learning-Based Smart Healthcare Systems—0
Graphfool: Targeted Label Adversarial Attack on Graph Embedding—0
Deep-RBF Networks Revisited: Robust Classification with Rejection—0
GraphMU: Repairing Robustness of Graph Neural Networks via Machine Unlearning—0
A Differentiable Language Model Adversarial Attack on Text Classifiers—0
DeepPeep: Exploiting Design Ramifications to Decipher the Architecture of Compact DNNs—0
Show:102550
← PrevPage 87 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