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 1–50 of 176 papers

TitleStatusHype
Safety at Scale: A Comprehensive Survey of Large Model SafetyCode3
Entangled Watermarks as a Defense against Model ExtractionCode1
MEME: Generating RNN Model Explanations via Model ExtractionCode1
MEA-Defender: A Robust Watermark against Model Extraction AttackCode1
"Yes, My LoRD." Guiding Language Model Extraction with Locality Reinforced DistillationCode1
Cryptanalytic Extraction of Neural Network ModelsCode1
Data-Free Model ExtractionCode1
ATOM: A Framework of Detecting Query-Based Model Extraction Attacks for Graph Neural NetworksCode1
Are You Copying My Model? Protecting the Copyright of Large Language Models for EaaS via Backdoor WatermarkCode1
Model Extraction and Adversarial Transferability, Your BERT is Vulnerable!Code1
MARLeME: A Multi-Agent Reinforcement Learning Model Extraction LibraryCode1
Black-Box Attacks on Sequential Recommenders via Data-Free Model ExtractionCode1
Neural Honeytrace: A Robust Plug-and-Play Watermarking Framework against Model Extraction AttacksCode1
Protecting Language Generation Models via Invisible WatermarkingCode1
FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model ExtractionCode1
MEME: Generating RNN Model Explanations via Model ExtractionCode1
Now You See Me (CME): Concept-based Model ExtractionCode1
Watermarking Vision-Language Pre-trained Models for Multi-modal Embedding as a ServiceCode1
Adversarial Exploitation of Policy Imitation—0
Better Decisions through the Right Causal World Model—0
Enhancing TinyML Security: Study of Adversarial Attack Transferability—0
A Knowledge Representation Approach to Automated Mathematical Modelling—0
AUTOLYCUS: Exploiting Explainable AI (XAI) for Model Extraction Attacks against Interpretable Models—0
A Desynchronization-Based Countermeasure Against Side-Channel Analysis of Neural Networks—0
Automated Data-Driven Model Extraction and Validation of Inverter Dynamics with Grid Support Function—0
Automating Agential Reasoning: Proof-Calculi and Syntactic Decidability for STIT Logics—0
Efficient Model Extraction via Boundary Sampling—0
Beyond Labeling Oracles: What does it mean to steal ML models?—0
Student Surpasses Teacher: Imitation Attack for Black-Box NLP APIs—0
An anatomy-based V1 model: Extraction of Low-level Features, Reduction of distortion and a V1-inspired SOM—0
Emerging AI Security Threats for Autonomous Cars -- Case Studies—0
Entangled Threats: A Unified Kill Chain Model for Quantum Machine Learning Security—0
A Survey on Event-based News Narrative Extraction—0
A Framework for Understanding Model Extraction Attack and Defense—0
DynaMarks: Defending Against Deep Learning Model Extraction Using Dynamic Watermarking—0
A framework for the extraction of Deep Neural Networks by leveraging public data—0
DualCF: Efficient Model Extraction Attack from Counterfactual Explanations—0
Efficiently Learning Any One Hidden Layer ReLU Network From Queries—0
CopyQNN: Quantum Neural Network Extraction Attack under Varying Quantum Noise—0
A Review of Confidentiality Threats Against Embedded Neural Network Models—0
Data-Free Model Extraction Attacks in the Context of Object Detection—0
Data-Free Model-Related Attacks: Unleashing the Potential of Generative AI—0
DeepNcode: Encoding-Based Protection against Bit-Flip Attacks on Neural Networks—0
A Survey of Model Extraction Attacks and Defenses in Distributed Computing Environments—0
DeepSteal: Advanced Model Extractions Leveraging Efficient Weight Stealing in Memories—0
Defending against Data-Free Model Extraction by Distributionally Robust Defensive Training—0
A Framework for Double-Blind Federated Adaptation of Foundation Models—0
Defending against Data-Free Model Extraction by Distributionally Robust Defensive Training—0
Differentially private fine-tuned NF-Net to predict GI cancer type—0
CaBaGe: Data-Free Model Extraction using ClAss BAlanced Generator Ensemble—0
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Benchmark Results

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