U Can't Gen This? A Survey of Intellectual Property Protection Methods for Data in Generative AI

TL;DR

This survey reviews the rapid development of IP protection methods for data used in generative AI, highlighting the challenges and categorizing technical solutions to prevent copyright violations.

Contribution

It introduces a taxonomy for systematically reviewing technical solutions aimed at protecting intellectual property in generative AI training data.

Findings

Provides a comprehensive taxonomy of IP protection methods

Identifies key challenges in safeguarding training data

Summarizes recent technical solutions and their effectiveness

Abstract

Large Generative AI (GAI) models have the unparalleled ability to generate text, images, audio, and other forms of media that are increasingly indistinguishable from human-generated content. As these models often train on publicly available data, including copyrighted materials, art and other creative works, they inadvertently risk violating copyright and misappropriation of intellectual property (IP). Due to the rapid development of generative AI technology and pressing ethical considerations from stakeholders, protective mechanisms and techniques are emerging at a high pace but lack systematisation. In this paper, we study the concerns regarding the intellectual property rights of training data and specifically focus on the properties of generative models that enable misuse leading to potential IP violations. Then we propose a taxonomy that leads to a systematic review of technical solutions for safeguarding the data from intellectual property violations in GAI.