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 1176–1200 of 1808 papers

TitleStatusHype
An Improved Genetic Algorithm and Its Application in Neural Network Adversarial AttackCode0
Adversarial defenses via a mixture of generators—0
Evaluating Deep Learning Models and Adversarial Attacks on Accelerometer-Based Gesture Authentication—0
Rethinking Adversarial Transferability from a Data Distribution Perspective—0
Neural Networks Playing Dough: Investigating Deep Cognition With a Gradient-Based Adversarial Attack—0
NODEAttack: Adversarial Attack on the Energy Consumption of Neural ODEs—0
Empirical Study of the Decision Region and Robustness in Deep Neural Networks—0
Fooling Adversarial Training with Induction Noise—0
-Weighted Federated Adversarial Training—0
One for Many: an Instagram inspired black-box adversarial attack—0
Linear Backpropagation Leads to Faster Convergence—0
Stochastic Variance Reduced Ensemble Adversarial Attack—0
Adversarially Robust Conformal Prediction—0
Large-Scale Adversarial Attacks on Graph Neural Networks via Graph Coarsening—0
A Branch and Bound Framework for Stronger Adversarial Attacks of ReLU Networks—0
Pixab-CAM: Attend Pixel, not Channel—0
Aug-ILA: More Transferable Intermediate Level Attacks with Augmented References—0
Cluster Attack: Query-based Adversarial Attacks on Graphs with Graph-Dependent PriorsCode0
Breaking BERT: Understanding its Vulnerabilities for Named Entity Recognition through Adversarial AttackCode0
Exploring Adversarial Examples for Efficient Active Learning in Machine Learning Classifiers—0
Robust Physical-World Attacks on Face Recognition—0
Universal Adversarial Attack on Deep Learning Based Prognostics—0
Improving Gradient-based Adversarial Training for Text Classification by Contrastive Learning and Auto-Encoder—0
A Practical Adversarial Attack on Contingency Detection of Smart Energy Systems—0
Improving the Robustness of Adversarial Attacks Using an Affine-Invariant Gradient Estimator—0
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All datasetsCIFAR-10CIFAR-100

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet20Test Accuracy89.9589.95(1)Community Verified
2Xu et al.Attack: PGD2078.68—Unverified
33-ensemble of multi-resolution self-ensemblesAttack: AutoAttack78.13—Unverified
4TRADES-ANCRA/ResNet18Attack: AutoAttack59.7—Unverified
5AdvTraining [madry2018]Attack: PGD2048.44—Unverified
6TRADES [zhang2019b]Attack: PGD2045.9—Unverified
7XU-NetRobust Accuracy1—Unverified
#ModelMetricClaimedVerifiedStatus
13-ensemble of multi-resolution self-ensemblesAttack: AutoAttack51.28—Unverified
2multi-resolution self-ensemblesAttack: AutoAttack47.85—Unverified