{"title":"Semi-Fragile Neural Network Watermarking Based on Adversarial Examples","authors":"Zihan Yuan;Xinpeng Zhang;Zichi Wang;Zhaoxia Yin","doi":"10.1109/TETCI.2024.3372373","DOIUrl":null,"url":null,"abstract":"Deep neural networks (DNNs) may be subject to various modifications during transmission and use. Regular processing operations do not affect the functionality of a model, while malicious tampering will cause serious damage. Therefore, it is crucial to determine the availability of a DNN model. To address this issue, we propose a semi-fragile black-box watermarking method that can distinguish between accidental modification and malicious tampering of DNNs, focusing on the privacy and security of neural network models. Specifically, for a given model, a strategy is designed to generate semi-fragile and sensitive samples using adversarial example techniques without decreasing the model accuracy. The model outputs for these samples are extremely sensitive to malicious tampering and robust to accidental modification. According to these properties, accidental modification and malicious tampering can be distinguished to assess the availability of a watermarked model. Extensive experiments demonstrate that the proposed method can detect malicious model tampering with high accuracy up to 100% while tolerating accidental modifications such as fine-tuning, pruning, and quantitation with the accuracy exceed 75%. Moreover, our semi-fragile neural network watermarking approach can be easily extended to various DNNs.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":5.3000,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Emerging Topics in Computational Intelligence","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10474363/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Deep neural networks (DNNs) may be subject to various modifications during transmission and use. Regular processing operations do not affect the functionality of a model, while malicious tampering will cause serious damage. Therefore, it is crucial to determine the availability of a DNN model. To address this issue, we propose a semi-fragile black-box watermarking method that can distinguish between accidental modification and malicious tampering of DNNs, focusing on the privacy and security of neural network models. Specifically, for a given model, a strategy is designed to generate semi-fragile and sensitive samples using adversarial example techniques without decreasing the model accuracy. The model outputs for these samples are extremely sensitive to malicious tampering and robust to accidental modification. According to these properties, accidental modification and malicious tampering can be distinguished to assess the availability of a watermarked model. Extensive experiments demonstrate that the proposed method can detect malicious model tampering with high accuracy up to 100% while tolerating accidental modifications such as fine-tuning, pruning, and quantitation with the accuracy exceed 75%. Moreover, our semi-fragile neural network watermarking approach can be easily extended to various DNNs.
期刊介绍:
The IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI) publishes original articles on emerging aspects of computational intelligence, including theory, applications, and surveys.
TETCI is an electronics only publication. TETCI publishes six issues per year.
Authors are encouraged to submit manuscripts in any emerging topic in computational intelligence, especially nature-inspired computing topics not covered by other IEEE Computational Intelligence Society journals. A few such illustrative examples are glial cell networks, computational neuroscience, Brain Computer Interface, ambient intelligence, non-fuzzy computing with words, artificial life, cultural learning, artificial endocrine networks, social reasoning, artificial hormone networks, computational intelligence for the IoT and Smart-X technologies.