Tokenized Model: A Blockchain-Empowered Decentralized Model Ownership Verification Platform

TL;DR

This paper proposes Tokenized Model, a blockchain-based platform that combines watermarking technology to verify and protect deep learning model ownership, enabling secure transactions and contribution tracking.

Contribution

It introduces a novel unified platform integrating model watermarking and blockchain for copyright protection and financial valuation of deep learning models.

Findings

Effective ownership verification demonstrated in case studies

Secure and transparent model transaction process established

Enhanced protection against unauthorized model use

Abstract

With the development of practical deep learning models like generative AI, their excellent performance has brought huge economic value. For instance, ChatGPT has attracted more than 100 million users in three months. Since the model training requires a lot of data and computing power, a well-performing deep learning model is behind a huge effort and cost. Facing various model attacks, unauthorized use and abuse from the network that threaten the interests of model owners, in addition to considering legal and other administrative measures, it is equally important to protect the model's copyright from the technical means. By using the model watermarking technology, we point out the possibility of building a unified platform for model ownership verification. Given the application history of blockchain in copyright verification and the drawbacks of a centralized third-party, this paper considers combining model watermarking technology and blockchain to build a unified model copyright protection platform. By a new solution we called Tokenized Model, it protects the model's copyright by reliable ownership record and verification mechanism. It also promotes the financial value of model by constructing the model's transaction process and contribution shares of a model. In the typical case study, we also study the various performance under usual scenario to verify the effectiveness of this platform.