Robust Watermarking on Gradient Boosting Decision Trees

📝 Original Info

• Title: Robust Watermarking on Gradient Boosting Decision Trees
• ArXiv ID: 2511.09822
• Date: 2025-11-12
• Authors: ** 정보가 제공되지 않았습니다. (논문 원문 또는 저자 페이지를 확인해 주세요.) **

📝 Abstract

Gradient Boosting Decision Trees (GBDTs) are widely used in industry and academia for their high accuracy and efficiency, particularly on structured data. However, watermarking GBDT models remains underexplored compared to neural networks. In this work, we present the first robust watermarking framework tailored to GBDT models, utilizing in-place fine-tuning to embed imperceptible and resilient watermarks. We propose four embedding strategies, each designed to minimize impact on model accuracy while ensuring watermark robustness. Through experiments across diverse datasets, we demonstrate that our methods achieve high watermark embedding rates, low accuracy degradation, and strong resistance to post-deployment fine-tuning.

💡 Deep Analysis

📄 Full Content

Reference