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 101–125 of 176 papers

TitleStatusHype
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
A Novel Watermarking Framework for Ownership Verification of DNN Architectures—0
NASPY: Automated Extraction of Automated Machine Learning Models—0
Was my Model Stolen? Feature Sharing for Robust and Transferable Watermarks—0
Emerging AI Security Threats for Autonomous Cars -- Case Studies—0
Black-Box Attacks on Sequential Recommenders via Data-Free Model ExtractionCode1
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
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Benchmark Results

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