Lightweight CNN Model Hashing with Higher-Order Statistics and Chaotic Mapping for Piracy Detection and Tamper Localization

TL;DR

This paper introduces a lightweight, training-free CNN hashing method that combines higher-order statistical features with chaotic mapping to detect piracy and localize tampering in neural network models effectively.

Contribution

It presents a novel approach that does not require additional neural network training, using HOS features and chaotic mapping for robust model hashing and tamper localization.

Findings

Effective piracy detection demonstrated on various CNN models.

High accuracy in tamper localization achieved.

Method is computationally efficient and easy to implement.

Abstract

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.