With the widespread adoption of deep neural networks (DNNs), protecting intellectual property and detecting unauthorized tampering of models have become pressing challenges. Recently, Perceptual hashing has emerged as an effective approach for identifying pirated models. However, existing methods either rely on neural networks for feature extraction, demanding substantial training resources, or suffer from limited applicability and cannot be universally applied to all convolutional neural networks (CNNs). To address these limitations, we propose a lightweight CNN model hashing technique that integrates higher-order statistics (HOS) features with a chaotic mapping mechanism. Without requiring any auxiliary neural network training, our method enables efficient piracy detection and precise tampering localization. Specifically, we extract skewness, kurtosis, and structural features from the parameters of each network layer to construct a model hash that is both robust and discriminative. Additionally, we introduce chaotic mapping to amplify minor changes in model parameters by exploiting the sensitivity of chaotic systems to initial conditions, thereby facilitating accurate localization of tampered regions. Experimental results validate the effectiveness and practical value of the proposed method for model copyright protection and integrity verification.

翻译：随着深度神经网络（DNNs）的广泛应用，保护模型知识产权并检测未经授权的篡改已成为紧迫挑战。近年来，感知哈希已成为识别盗版模型的有效方法。然而，现有方法要么依赖神经网络进行特征提取，需要大量训练资源，要么适用性有限，无法普遍应用于所有卷积神经网络（CNNs）。为克服这些局限，本文提出一种轻量级CNN模型哈希技术，将高阶统计（HOS）特征与混沌映射机制相结合。该方法无需任何辅助神经网络训练，即可实现高效的盗版检测与精确的篡改定位。具体而言，我们从网络各层参数中提取偏度、峰度及结构特征，构建兼具鲁棒性与区分度的模型哈希。此外，通过利用混沌系统对初始条件的敏感性，引入混沌映射以放大模型参数的细微变化，从而实现对篡改区域的精确定位。实验结果验证了所提方法在模型版权保护与完整性验证方面的有效性与实用价值。