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 1126–1150 of 1808 papers

TitleStatusHype
MedAttacker: Exploring Black-Box Adversarial Attacks on Risk Prediction Models in Healthcare—0
How Private Is Your RL Policy? An Inverse RL Based Analysis FrameworkCode0
Learning to Learn Transferable AttackCode0
Amicable Aid: Perturbing Images to Improve Classification Performance—0
SNEAK: Synonymous Sentences-Aware Adversarial Attack on Natural Language Video Localization—0
ML Attack Models: Adversarial Attacks and Data Poisoning Attacks—0
Exploring the Vulnerability of Natural Language Processing Models via Universal Adversarial TextsCode0
Pyramid Adversarial Training Improves ViT PerformanceCode0
MedRDF: A Robust and Retrain-Less Diagnostic Framework for Medical Pretrained Models Against Adversarial Attack—0
Adaptive Image Transformations for Transfer-based Adversarial AttackCode0
Adaptive Perturbation for Adversarial Attack—0
Natural & Adversarial Bokeh Rendering via Circle-of-Confusion Predictive Network—0
Thundernna: a white box adversarial attack—0
Heterogeneous Architecture Search Approach within Adversarial Dynamic Defense Framework—0
Metamorphic Adversarial Detection Pipeline for Face Recognition Systems—0
A Practical and Stealthy Adversarial Attack for Cyber-Physical Applications—0
Enhanced countering adversarial attacks via input denoising and feature restoringCode0
Fooling Adversarial Training with Inducing Noise—0
Generating Unrestricted 3D Adversarial Point CloudsCode0
Self-Supervised Contrastive Learning with Adversarial Perturbations for Robust Pretrained Language Models—0
Robust and Effective Grammatical Error Correction with Simple Cycle Self-Augmenting—0
Perturbations in the Wild: Leveraging Human-Written Text Perturbations for Realistic Adversarial Attack and Defense—0
BufferSearch: Generating Black-Box Adversarial Texts With Lower Queries—0
Improving the robustness and accuracy of biomedical language models through adversarial trainingCode0
Robustness of Bayesian Neural Networks to White-Box Adversarial Attacks—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