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

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Showing 1–50 of 176 papers

TitleStatusHype
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
On the interplay of Explainability, Privacy and Predictive Performance with Explanation-assisted Model Extraction—0
Better Decisions through the Right Causal World Model—0
CopyQNN: Quantum Neural Network Extraction Attack under Varying Quantum Noise—0
ATOM: A Framework of Detecting Query-Based Model Extraction Attacks for Graph Neural NetworksCode1
ProDiF: Protecting Domain-Invariant Features to Secure Pre-Trained Models Against Extraction—0
A Survey of Model Extraction Attacks and Defenses in Distributed Computing Environments—0
Differentially private fine-tuned NF-Net to predict GI cancer type—0
From Counterfactuals to Trees: Competitive Analysis of Model Extraction AttacksCode0
A Framework for Double-Blind Federated Adaptation of Foundation Models—0
Safety at Scale: A Comprehensive Survey of Large Model SafetyCode3
Data-Free Model-Related Attacks: Unleashing the Potential of Generative AI—0
"FRAME: Forward Recursive Adaptive Model Extraction -- A Technique for Advance Feature Selection"—0
Neural Honeytrace: A Robust Plug-and-Play Watermarking Framework against Model Extraction AttacksCode1
HoneypotNet: Backdoor Attacks Against Model Extraction—0
Bounding-box Watermarking: Defense against Model Extraction Attacks on Object Detectors—0
Few-shot Model Extraction Attacks against Sequential Recommender Systems—0
A Hard-Label Cryptanalytic Extraction of Non-Fully Connected Deep Neural Networks using Side-Channel AttacksCode0
Your Semantic-Independent Watermark is Fragile: A Semantic Perturbation Attack against EaaS WatermarkCode0
Robust and Minimally Invasive Watermarking for EaaSCode0
Efficient Model Extraction via Boundary Sampling—0
Efficient and Effective Model ExtractionCode0
CaBaGe: Data-Free Model Extraction using ClAss BAlanced Generator Ensemble—0
Protecting Copyright of Medical Pre-trained Language Models: Training-Free Backdoor Model Watermarking—0
"Yes, My LoRD." Guiding Language Model Extraction with Locality Reinforced DistillationCode1
VidModEx: Interpretable and Efficient Black Box Model Extraction for High-Dimensional SpacesCode0
Enhancing TinyML Security: Study of Adversarial Attack Transferability—0
QUEEN: Query Unlearning against Model Extraction—0
Privacy Implications of Explainable AI in Data-Driven Systems—0
Beyond Slow Signs in High-fidelity Model ExtractionCode0
GENIE: Watermarking Graph Neural Networks for Link Prediction—0
Watermarking Counterfactual ExplanationsCode0
Noisy Data Meets Privacy: Training Local Models with Post-Processed Remote Queries—0
DeepNcode: Encoding-Based Protection against Bit-Flip Attacks on Neural Networks—0
Model Reconstruction Using Counterfactual Explanations: A Perspective From Polytope TheoryCode0
Learnable Linguistic Watermarks for Tracing Model Extraction Attacks on Large Language Models—0
Knowledge Distillation-Based Model Extraction Attack using GAN-based Private Counterfactual ExplanationsCode0
QuantumLeak: Stealing Quantum Neural Networks from Cloud-based NISQ Machines—0
Not Just Change the Labels, Learn the Features: Watermarking Deep Neural Networks with Multi-View DataCode0
Precise Extraction of Deep Learning Models via Side-Channel Attacks on Edge/Endpoint Devices—0
WARDEN: Multi-Directional Backdoor Watermarks for Embedding-as-a-Service Copyright ProtectionCode0
MEA-Defender: A Robust Watermark against Model Extraction AttackCode1
Unraveling Attacks in Machine Learning-based IoT Ecosystems: A Survey and the Open Libraries Behind Them—0
MEAOD: Model Extraction Attack against Object Detectors—0
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Benchmark Results

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