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 1211–1220 of 1808 papers

TitleStatusHype
Feature-Filter: Detecting Adversarial Examples through Filtering off Recessive Features—0
Feature Importance Guided Attack: A Model Agnostic Adversarial Attack—0
Feature Unlearning for Pre-trained GANs and VAEs—0
Feature Visualization within an Automated Design Assessment leveraging Explainable Artificial Intelligence Methods—0
FedDef: Defense Against Gradient Leakage in Federated Learning-based Network Intrusion Detection Systems—0
Few-Features Attack to Fool Machine Learning Models through Mask-Based GAN—0
Learning Transferable Adversarial Robust Representations via Multi-view Consistency—0
F&F Attack: Adversarial Attack against Multiple Object Trackers by Inducing False Negatives and False Positives—0
FineFool: Fine Object Contour Attack via Attention—0
FlippedRAG: Black-Box Opinion Manipulation Adversarial Attacks to Retrieval-Augmented Generation Models—0
Show:102550
← PrevPage 122 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