SOTAVerified

Model extraction

Model extraction attacks, aka model stealing attacks, are used to extract the parameters from the target model. Ideally, the adversary will be able to steal and replicate a model that will have a very similar performance to the target model.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 121–130 of 176 papers

TitleStatusHype
Student Surpasses Teacher: Imitation Attack for Black-Box NLP APIs—0
Power-Based Attacks on Spatial DNN Accelerators—0
MEGEX: Data-Free Model Extraction Attack against Gradient-Based Explainable AI—0
Stateful Detection of Model Extraction AttacksCode0
HODA: Hardness-Oriented Detection of Model Extraction Attacks—0
Model Extraction and Adversarial Attacks on Neural Networks using Switching Power Information—0
Killing One Bird with Two Stones: Model Extraction and Attribute Inference Attacks against BERT-based APIs—0
An Exact Poly-Time Membership-Queries Algorithm for Extraction a three-Layer ReLU Network—0
A Review of Confidentiality Threats Against Embedded Neural Network Models—0
Good Artists Copy, Great Artists Steal: Model Extraction Attacks Against Image Translation Models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1three-step-originalExact Match0.17—Unverified