中图分类号：TP181
文献标识码：ADOI:10.19358/j.issn.2097-1788.2023.12.001
引用格式：屈详颜，于静，熊刚,等.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.

Copyright protection for deep learning models utilizing a black box testing framework

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 generation 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 adversarial attacks. Additionally, new test metrics and algorithms are introduced for blackbox scenarios, enhancing the defensive capabilities 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 correlated indicators.

Key words : generative artificial intelligence; deep learning models; copyright protection; black box defense