Xintao Duan1,2,*, Yinhang Wu1, Zhao Wang1, Chuan Qin3
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4887-4906, 2025, DOI:10.32604/cmc.2025.064620
- 30 July 2025
Abstract Deep neural network (DNN) models have achieved remarkable performance across diverse tasks, leading to widespread commercial adoption. However, training high-accuracy models demands extensive data, substantial computational resources, and significant time investment, making them valuable assets vulnerable to unauthorized exploitation. To address this issue, this paper proposes an intellectual property (IP) protection framework for DNN models based on feature layer selection and hyper-chaotic mapping. Firstly, a sensitivity-based importance evaluation algorithm is used to identify the key feature layers for encryption, effectively protecting the core components of the model. Next, the L1 regularization criterion is applied to More >