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 51–60 of 176 papers

TitleStatusHype
SAME: Sample Reconstruction against Model Extraction AttacksCode0
Model Extraction Attacks Revisited—0
Security and Privacy Challenges in Deep Learning Models—0
Watermarking Vision-Language Pre-trained Models for Multi-modal Embedding as a ServiceCode1
Army of Thieves: Enhancing Black-Box Model Extraction via Ensemble based sample selectionCode0
Like an Open Book? Read Neural Network Architecture with Simple Power Analysis on 32-bit Microcontrollers—0
Defense Against Model Extraction Attacks on Recommender SystemsCode0
MeaeQ: Mount Model Extraction Attacks with Efficient QueriesCode0
Towards dialogue based, computer aided software requirements elicitation—0
SCME: A Self-Contrastive Method for Data-free and Query-Limited Model Extraction Attack—0
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Benchmark Results

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