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 101–150 of 176 papers

TitleStatusHype
Quantifying (Hyper) Parameter Leakage in Machine Learning—0
QuantumLeak: Stealing Quantum Neural Networks from Cloud-based NISQ Machines—0
QUEEN: Query Unlearning against Model Extraction—0
Revealing Secrets From Pre-trained Models—0
SCME: A Self-Contrastive Method for Data-free and Query-Limited Model Extraction Attack—0
Security and Privacy Challenges in Deep Learning Models—0
Seeds Don't Lie: An Adaptive Watermarking Framework for Computer Vision Models—0
SEEK: model extraction attack against hybrid secure inference protocols—0
Split HE: Fast Secure Inference Combining Split Learning and Homomorphic Encryption—0
Stealing Deep Reinforcement Learning Models for Fun and Profit—0
Thief, Beware of What Get You There: Towards Understanding Model Extraction Attack—0
Three-dimensional planar model estimation using multi-constraint knowledge based on k-means and RANSAC—0
Towards dialogue based, computer aided software requirements elicitation—0
Towards Few-Call Model Stealing via Active Self-Paced Knowledge Distillation and Diffusion-Based Image Generation—0
Towards Security Threats of Deep Learning Systems: A Survey—0
Unraveling Attacks in Machine Learning-based IoT Ecosystems: A Survey and the Open Libraries Behind Them—0
Using Python for Model Inference in Deep Learning—0
Was my Model Stolen? Feature Sharing for Robust and Transferable Watermarks—0
Watermarking Graph Neural Networks based on Backdoor Attacks—0
Sparsity-driven Digital Terrain Model Extraction—0
GradEscape: A Gradient-Based Evader Against AI-Generated Text Detectors—0
A Desynchronization-Based Countermeasure Against Side-Channel Analysis of Neural Networks—0
Adversarial Exploitation of Policy Imitation—0
Adversarial Model Extraction on Graph Neural Networks—0
A Framework for Double-Blind Federated Adaptation of Foundation Models—0
A framework for the extraction of Deep Neural Networks by leveraging public data—0
A Framework for Understanding Model Extraction Attack and Defense—0
A Knowledge Representation Approach to Automated Mathematical Modelling—0
An anatomy-based V1 model: Extraction of Low-level Features, Reduction of distortion and a V1-inspired SOM—0
An Exact Poly-Time Membership-Queries Algorithm for Extraction a three-Layer ReLU Network—0
A Novel Watermarking Framework for Ownership Verification of DNN Architectures—0
A Practical Introduction to Side-Channel Extraction of Deep Neural Network Parameters—0
A Review of Confidentiality Threats Against Embedded Neural Network Models—0
A Survey of Model Extraction Attacks and Defenses in Distributed Computing Environments—0
A Survey on Event-based News Narrative Extraction—0
AUTOLYCUS: Exploiting Explainable AI (XAI) for Model Extraction Attacks against Interpretable Models—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
Better Decisions through the Right Causal World Model—0
Beyond Labeling Oracles: What does it mean to steal ML models?—0
Student Surpasses Teacher: Imitation Attack for Black-Box NLP APIs—0
BODAME: Bilevel Optimization for Defense Against Model Extraction—0
Bounding-box Watermarking: Defense against Model Extraction Attacks on Object Detectors—0
Bound Your Models! How to Make OWL an ASP Modeling Language—0
Business Process Text Sketch Automation Generation Using Large Language Model—0
CaBaGe: Data-Free Model Extraction using ClAss BAlanced Generator Ensemble—0
CopyQNN: Quantum Neural Network Extraction Attack under Varying Quantum Noise—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
Show:102550
← PrevPage 3 of 4Next →

Benchmark Results

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