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
Seeds Don't Lie: An Adaptive Watermarking Framework for Computer Vision Models—0
A Practical Introduction to Side-Channel Extraction of Deep Neural Network Parameters—0
Towards Automatically Extracting UML Class Diagrams from Natural Language SpecificationsCode0
SEEK: model extraction attack against hybrid secure inference protocols—0
DynaMarks: Defending Against Deep Learning Model Extraction Using Dynamic Watermarking—0
Revealing Secrets From Pre-trained Models—0
EVE: Environmental Adaptive Neural Network Models for Low-power Energy Harvesting System—0
On the amplification of security and privacy risks by post-hoc explanations in machine learning models—0
A Framework for Understanding Model Extraction Attack and Defense—0
On the Difficulty of Defending Self-Supervised Learning against Model ExtractionCode0
DualCF: Efficient Model Extraction Attack from Counterfactual Explanations—0
Stealing and Evading Malware Classifiers and Antivirus at Low False Positive ConditionsCode0
Split HE: Fast Secure Inference Combining Split Learning and Homomorphic Encryption—0
On the Effectiveness of Dataset Watermarking in Adversarial SettingsCode0
Fingerprinting Deep Neural Networks Globally via Universal Adversarial Perturbations—0
Increasing the Cost of Model Extraction with Calibrated Proof of Work—0
Protecting Intellectual Property of Language Generation APIs with Lexical WatermarkCode0
Efficiently Learning One Hidden Layer ReLU Networks From Queries—0
Efficiently Learning Any One Hidden Layer ReLU Network From Queries—0
DeepSteal: Advanced Model Extractions Leveraging Efficient Weight Stealing in Memories—0
Watermarking Graph Neural Networks based on Backdoor Attacks—0
Process Extraction from Text: Benchmarking the State of the Art and Paving the Way for Future ChallengesCode0
First to Possess His Statistics: Data-Free Model Extraction Attack on Tabular Data—0
HODA: Protecting DNNs Against Model Extraction Attacks via Hardness of Samples—0
NASPY: Automated Extraction of Automated Machine Learning Models—0
A Novel Watermarking Framework for Ownership Verification of DNN Architectures—0
Was my Model Stolen? Feature Sharing for Robust and Transferable Watermarks—0
Emerging AI Security Threats for Autonomous Cars -- Case Studies—0
Student Surpasses Teacher: Imitation Attack for Black-Box NLP APIs—0
Power-Based Attacks on Spatial DNN Accelerators—0
MEGEX: Data-Free Model Extraction Attack against Gradient-Based Explainable AI—0
Stateful Detection of Model Extraction AttacksCode0
HODA: Hardness-Oriented Detection of Model Extraction Attacks—0
Model Extraction and Adversarial Attacks on Neural Networks using Switching Power Information—0
Killing One Bird with Two Stones: Model Extraction and Attribute Inference Attacks against BERT-based APIs—0
An Exact Poly-Time Membership-Queries Algorithm for Extraction a three-Layer ReLU Network—0
A Review of Confidentiality Threats Against Embedded Neural Network Models—0
Good Artists Copy, Great Artists Steal: Model Extraction Attacks Against Image Translation Models—0
Thief, Beware of What Get You There: Towards Understanding Model Extraction Attack—0
Using Python for Model Inference in Deep Learning—0
BODAME: Bilevel Optimization for Defense Against Model Extraction—0
Model Extraction and Defenses on Generative Adversarial Networks—0
Grey-box Extraction of Natural Language Models—0
EXPLORING VULNERABILITIES OF BERT-BASED APIS—0
Sparsity-driven Digital Terrain Model Extraction—0
A Knowledge Representation Approach to Automated Mathematical Modelling—0
Monitoring-based Differential Privacy Mechanism Against Query-Flooding Parameter Duplication Attack—0
Leveraging Extracted Model Adversaries for Improved Black Box Attacks—0
Model Extraction Attacks on Graph Neural Networks: Taxonomy and RealizationCode0
Model extraction from counterfactual explanationsCode0
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Benchmark Results

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