Deep Intellectual Property: A Survey

by   Yuchen Sun, et al.

With the widespread application in industrial manufacturing and commercial services, well-trained deep neural networks (DNNs) are becoming increasingly valuable and crucial assets due to the tremendous training cost and excellent generalization performance. These trained models can be utilized by users without much expert knowledge benefiting from the emerging ”Machine Learning as a Service” (MLaaS) paradigm. However, this paradigm also exposes the expensive models to various potential threats like model stealing and abuse. As an urgent requirement to defend against these threats, Deep Intellectual Property (DeepIP), to protect private training data, painstakingly-tuned hyperparameters, or costly learned model weights, has been the consensus of both industry and academia. To this end, numerous approaches have been proposed to achieve this goal in recent years, especially to prevent or discover model stealing and unauthorized redistribution. Given this period of rapid evolution, the goal of this paper is to provide a comprehensive survey of the recent achievements in this field. More than 190 research contributions are included in this survey, covering many aspects of Deep IP Protection: challenges/threats, invasive solutions (watermarking), non-invasive solutions (fingerprinting), evaluation metrics, and performance. We finish the survey by identifying promising directions for future research.


page 2

page 3

page 4

page 7

page 21

page 22

page 23

page 37


Deep Learning for Generic Object Detection: A Survey

Generic object detection, aiming at locating object instances from a lar...

DNN Intellectual Property Protection: Taxonomy, Methods, Attack Resistance, and Evaluations

The training and creation of deep learning model is usually costly, thus...

Performance Comparison of Contemporary DNN Watermarking Techniques

DNNs shall be considered as the intellectual property (IP) of the model ...

I Know What You Trained Last Summer: A Survey on Stealing Machine Learning Models and Defences

Machine Learning-as-a-Service (MLaaS) has become a widespread paradigm, ...

A Survey on Model Watermarking Neural Networks

Machine learning (ML) models are applied in an increasing variety of dom...

A Survey on Device Behavior Fingerprinting: Data Sources, Techniques, Application Scenarios, and Datasets

In the current network-based computing world, where the number of interc...

Please sign up or login with your details

Forgot password? Click here to reset