Work

A Blockchain-Enhanced Reversible Watermarking Framework for End-to-End Data Traceability in Federated Learning Systems

Year: 2025

Type: conference-paper

Abstract: In federated learning (FL) environments, ensuring data traceability presents significant challenges, particularly when data move between multiple entities such as data centers, edge nodes, and data scientists. This paper presents a novel framework that combines robust reversible watermarking and blockchain technology to achieve end-to-end traceability of medical images in a FL context. Based on the watermark, it becomes possible to interrogate the blockchain about the life cycle of an image to ensure data traceability, authenticity, and integrity. We use a histogram shifting-based reversible watermarking scheme with a new overflow management procedure, integrated with a private blockchain that records all watermarking and verification operations. Experimental results demonstrate the effectiveness of our approach in terms of watermark robustness considering a chest X-ray image dataset. We further show that watermarking does not interfere in the training and inference phase of a VGG-16 classification model for a Covid-19 medical database. A model trained on protected data can be used to classify non-watermarked data as well.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.15656416

Authors: Bellafqira, Reda, Berton, Chloé, Gouenou, Coatrieux

Institution: IMT Atlantique

Language: English

Topic: Advanced Steganography and Watermarking Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: European Commission

Award: Privacy Aware and Privacy Preserving Distributed and Robust Machine Learning for Medical Applications