BIMW: Blockchain-Enabled Innocuous Model Watermarking for Secure Ownership Verification

Xinyun Liu and Ronghua Xu ()
Additional contact information
Xinyun Liu: Department of Applied Computing, Michigan Technological University, Houghton, MI 49931, USA
Ronghua Xu: Department of Applied Computing, Michigan Technological University, Houghton, MI 49931, USA

Future Internet, 2025, vol. 17, issue 11, 1-21

Abstract: The integration of artificial intelligence (AI) and edge computing gives rise to edge intelligence (EI), which offers effective solutions to the limitations of traditional cloud-based AI; however, deploying models across distributed edge platforms raises concerns regarding authenticity, thereby necessitating robust mechanisms for ownership verification. Currently, backdoor-based model watermarking techniques represent a state-of-the-art approach for ownership verification; however, their reliance on model poisoning introduces potential security risks and unintended behaviors. To solve this challenge, we propose BIMW, a blockchain-enabled innocuous model watermarking framework that ensures secure and trustworthy AI model deployment and sharing in distributed edge computing environments. Unlike widely applied backdoor-based watermarking methods, BIMW adopts a novel innocuous model watermarking method called interpretable watermarking (IW), which embeds ownership information without compromising model integrity or functionality. In addition, BIMW integrates a blockchain security fabric to ensure the integrity and auditability of watermarked data during storage and sharing. Extensive experiments were conducted on a Jetson Orin Nano board, which simulates edge computing environments. The numerical results show that our framework outperforms baselines in terms of predicate accuracy, p -value, watermark success rate (WSR), and harmlessness H . Our framework demonstrates resilience against watermarking removal attacks, and it introduces limited latency through the blockchain fabric.

Keywords: ownership verification; model watermarking; deep neural networks; blockchain; edge computing (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1999-5903/17/11/490/pdf (application/pdf)
https://www.mdpi.com/1999-5903/17/11/490/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:17:y:2025:i:11:p:490-:d:1780051

Access Statistics for this article

Future Internet is currently edited by Ms. Grace You

More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().