网络安全与数据治理2023，Vol.42Issue(12)：1-6,13,7.DOI:10.19358/j.issn.2097-1788.2023.12.001
基于黑盒测试框架的深度学习模型版权保护方法
Copyright protection for deep learning models utilizing a black-box testing framework
摘要
Abstract
With the rapid development of generative artificial intelligence technologies,the copyright protection of deep learning models has become increasingly important.Existing copyright protection methods generally adopt deterministic test sample genera-tion algorithms,which suffer from inefficiencies in selection and vulnerabilities to adversarial attacks.To address these issues,we propose a copyright protection method for deep learning models based on a black-box testing framework.This method introduces a sample generation strategy based on randomness algorithms,effectively improving test efficiency and reducing the risk of adversari-al attacks.Additionally,new test metrics and algorithms are introduced for black-box scenarios,enhancing the defensive capabili-ties of black-box testing and ensuring each metric possesses sufficient orthogonality.In experimental validation,the proposed method demonstrates high efficiency in copyright judgment accuracy and reliability,effectively reducing the number of highly cor-related indicators.关键词
生成式人工智能/深度学习模型/版权保护/黑盒防御Key words
generative artificial intelligence/deep learning models/copyright protection/black box defense分类
信息技术与安全科学引用本文复制引用
屈详颜,于静,熊刚,盖珂珂..基于黑盒测试框架的深度学习模型版权保护方法[J].网络安全与数据治理,2023,42(12):1-6,13,7.基金项目
国家自然科学基金(62006222) （62006222）
