A Consumer-Oriented Image Transformation Scheme With a Secret Key for Privacy Protection

作     者：Zhao, Ruoyu Wen, Wenying Zhu, Youwen Lan, Rushi 

作者机构：Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Peoples R China Jiangxi Univ Finance & Econ Sch Informat Technol Nanchang 330013 Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Image & Graph Intelligent Proc Guilin 541004 Peoples R China 

出 版 物：《IEEE SIGNAL PROCESSING LETTERS》 (IEEE Signal Process Lett)

年 卷 期：2024年第31卷

页      面：2280-2284页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 

基　　金：National Key R&D Program of China [2021YFB3100400] Natural Science Foundation of China [62201233, 62172216] Double Thousand Plan of Jiangxi Province [jxsq2023201118] Outstanding Youth Fund Program of Jiangxi Province [t20232ACB212004] 

主　　题：Privacy Signal processing algorithms Semantics Perturbation methods Vectors Transform coding Protection Consumer friendliness facial image image transformation JPEG compatibility privacy preservation 

摘      要：Images in electronic devices may pose privacy threats since they can capture sensitive information about consumers. Meanwhile, face recognition (FR) systems are widely used, exacerbating these concerns. Some learning-based schemes have been proposed to protect the privacy of facial images. However, consumers might encounter challenges in implementing them due to specific requirements related to computing power and professional background. The non-learning semantic adversarial perturbation schemes address the aforementioned issues, but they are either irreversible or necessitate additional storage space. To this end, we propose a consumer-oriented image transformation scheme that can prevent the recognition of images by FR systems. It offers a consumer-friendly method compared to schemes that need high-performance equipment and specialized knowledge. The proposed scheme is implemented by reversible embedding the key to coefficients during the JPEG compression. The proposed scheme can be operated in general computing devices by ordinary consumers. The experiments demonstrated that facial images can be protected by the proposed scheme.