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

arXiv: https://arxiv.org/abs/2312.00048 · HTML: https://mlsanity.com/paper/2312.00048/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Watermarking generated content and models, one of 12 directions in LLM safety and security.
- Builds on 13 of its 16 references that are in the map.
- First posted on arXiv 27 Nov 2023.

## Placed in

- LLM safety and security / [Watermarking generated content and models](https://mlsanity.com/safety/watermarking-generated-content-and-models/)

## Builds on (most cited first, 13 of 13)

- [Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring](https://mlsanity.com/paper/1802.04633/) (2018)
- [The Stable Signature: Rooting Watermarks in Latent Diffusion Models](https://mlsanity.com/paper/2303.15435/) (2023)
- [Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples](https://mlsanity.com/paper/2302.04578/) (2023)
- [A Blockchain-based Decentralized Federated Learning Framework with Committee Consensus](https://mlsanity.com/paper/2004.00773/) (2020)
- [Are You Copying My Model? Protecting the Copyright of Large Language Models for EaaS via Backdoor Watermark](https://mlsanity.com/paper/2305.10036/) (2023)
- [An Overview of Backdoor Attacks Against Deep Neural Networks and Possible Defences](https://mlsanity.com/paper/2111.08429/) (2021)
- [FedIPR: Ownership Verification for Federated Deep Neural Network Models](https://mlsanity.com/paper/2109.13236/) (2021)
- [FedTracker: Furnishing Ownership Verification and Traceability for Federated Learning Model](https://mlsanity.com/paper/2211.07160/) (2022)
- [Non-Fungible Token (NFT): Overview, Evaluation, Opportunities and Challenges](https://mlsanity.com/paper/2105.07447/) (2021)
- [Model Extraction and Defenses on Generative Adversarial Networks](https://mlsanity.com/paper/2101.02069/) (2021)
- [A Blockchain-based Platform Architecture for Multimedia Data Management](https://mlsanity.com/paper/2009.03012/) (2020)
- [DCDChain: A Credible Architecture of Digital Copyright Detection Based on Blockchain](https://mlsanity.com/paper/2010.01235/) (2020)
- [Securing music sharing platforms: A Blockchain-Based Approach](https://mlsanity.com/paper/2110.05949/) (2021)

## Abstract (arXiv)

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.