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 1–25 of 176 papers

TitleStatusHype
Safety at Scale: A Comprehensive Survey of Large Model SafetyCode3
ATOM: A Framework of Detecting Query-Based Model Extraction Attacks for Graph Neural NetworksCode1
Neural Honeytrace: A Robust Plug-and-Play Watermarking Framework against Model Extraction AttacksCode1
"Yes, My LoRD." Guiding Language Model Extraction with Locality Reinforced DistillationCode1
MEA-Defender: A Robust Watermark against Model Extraction AttackCode1
Watermarking Vision-Language Pre-trained Models for Multi-modal Embedding as a ServiceCode1
Are You Copying My Model? Protecting the Copyright of Large Language Models for EaaS via Backdoor WatermarkCode1
Protecting Language Generation Models via Invisible WatermarkingCode1
FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model ExtractionCode1
Black-Box Attacks on Sequential Recommenders via Data-Free Model ExtractionCode1
Model Extraction and Adversarial Transferability, Your BERT is Vulnerable!Code1
MEME: Generating RNN Model Explanations via Model ExtractionCode1
Data-Free Model ExtractionCode1
Now You See Me (CME): Concept-based Model ExtractionCode1
MEME: Generating RNN Model Explanations via Model ExtractionCode1
MARLeME: A Multi-Agent Reinforcement Learning Model Extraction LibraryCode1
Cryptanalytic Extraction of Neural Network ModelsCode1
Entangled Watermarks as a Defense against Model ExtractionCode1
Entangled Threats: A Unified Kill Chain Model for Quantum Machine Learning Security—0
CEGA: A Cost-Effective Approach for Graph-Based Model Extraction and AcquisitionCode0
Navigating the Deep: Signature Extraction on Deep Neural Networks—0
Explore the vulnerability of black-box models via diffusion models—0
GradEscape: A Gradient-Based Evader Against AI-Generated Text Detectors—0
MISLEADER: Defending against Model Extraction with Ensembles of Distilled ModelsCode0
Evaluating Query Efficiency and Accuracy of Transfer Learning-based Model Extraction Attack in Federated Learning—0
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Benchmark Results

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