Model extraction

56 papers with code • 1 benchmarks • 2 datasets

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.

Libraries

Use these libraries to find Model extraction models and implementations

Most implemented papers

Entangled Watermarks as a Defense against Model Extraction

cleverhans-lab/entangled-watermark 27 Feb 2020

Such pairs are watermarks, which are not sampled from the task distribution and are only known to the defender.

FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model Extraction

aiot-mlsys-lab/fedrolex 3 Dec 2022

Most cross-device federated learning (FL) studies focus on the model-homogeneous setting where the global server model and local client models are identical.

Now You See Me (CME): Concept-based Model Extraction

dmitrykazhdan/CME 25 Oct 2020

Deep Neural Networks (DNNs) have achieved remarkable performance on a range of tasks.

Data-Free Model Extraction

cake-lab/datafree-model-extraction CVPR 2021

Current model extraction attacks assume that the adversary has access to a surrogate dataset with characteristics similar to the proprietary data used to train the victim model.

Process Extraction from Text: Benchmarking the State of the Art and Paving the Way for Future Challenges

patriziobellan86/processextractionfromtextsotaandchallenges 7 Oct 2021

The extraction of process models from text refers to the problem of turning the information contained in an unstructured textual process descriptions into a formal representation, i. e., a process model.

Protecting Language Generation Models via Invisible Watermarking

xuandongzhao/ginsew 6 Feb 2023

We can then detect the secret message by probing a suspect model to tell if it is distilled from the protected one.

"Yes, My LoRD." Guiding Language Model Extraction with Locality Reinforced Distillation

liangzid/lord-mea 4 Sep 2024

Model extraction attacks (MEAs) on large language models (LLMs) have received increasing attention in recent research.

Stealing Machine Learning Models via Prediction APIs

ftramer/Steal-ML 9 Sep 2016

In such attacks, an adversary with black-box access, but no prior knowledge of an ML model's parameters or training data, aims to duplicate the functionality of (i. e., "steal") the model.

An Approach for Process Model Extraction By Multi-Grained Text Classification

qianc62/MGTC 16 May 2019

Process model extraction (PME) is a recently emerged interdiscipline between natural language processing (NLP) and business process management (BPM), which aims to extract process models from textual descriptions.

DAWN: Dynamic Adversarial Watermarking of Neural Networks

ssg-research/dawn-dynamic-adversarial-watermarking-of-neural-networks 3 Jun 2019

Existing watermarking schemes are ineffective against IP theft via model extraction since it is the adversary who trains the surrogate model.