通过循环学习实现可逆匿名化,保护面部生物识别技术的隐私

IF 2.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS EURASIP Journal on Information Security Pub Date : 2024-08-05 DOI:10.1186/s13635-024-00174-3
Shuying Xu, Ching-Chun Chang, Huy H. Nguyen, Isao Echizen
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引用次数: 0

摘要

面部识别系统已成为身份验证不可或缺的组成部分。这些系统在很大程度上依赖于存储在生物识别数据库中的面部数据。然而,将这些数据存储在数据库中会引发隐私泄露问题。为了解决这个问题,人们提出了几种保护面部生物识别技术。遗憾的是,其中许多方法都会对数据造成不可逆转的破坏,导致数据无法用于其他目的。在本文中,我们提出了一种通过循环学习实现人脸图像可逆匿名化的新方案。在我们的方案中,为了保护隐私,人脸图像可以被去标识,并在必要时重新标识。为此,我们采用了具有循环一致性损失函数的生成对抗网络来学习去标识域和再标识域之间的双向转换。实验结果表明,我们的方案在去标识化和再标识化方面都表现出色。此外,安全分析验证了我们的系统在减轻潜在攻击方面的有效性。
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Reversible anonymization for privacy of facial biometrics via cyclic learning
Facial recognition systems have emerged as indispensable components in identity verification. These systems heavily rely on facial data, which is stored in a biometric database. However, storing such data in a database raises concerns about privacy breaches. To address this issue, several technologies have been proposed for protecting facial biometrics. Unfortunately, many of these methods can cause irreversible damage to the data, rendering it unusable for other purposes. In this paper, we propose a novel reversible anonymization scheme for face images via cyclic learning. In our scheme, face images can be de-identified for privacy protection and reidentified when necessary. To achieve this, we employ generative adversarial networks with a cycle consistency loss function to learn the bidirectional transformation between the de-identified and re-identified domains. Experimental results demonstrate that our scheme performs well in terms of both de-identification and reidentification. Furthermore, a security analysis validates the effectiveness of our system in mitigating potential attacks.
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来源期刊
EURASIP Journal on Information Security
EURASIP Journal on Information Security COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
8.80
自引率
0.00%
发文量
6
审稿时长
13 weeks
期刊介绍: The overall goal of the EURASIP Journal on Information Security, sponsored by the European Association for Signal Processing (EURASIP), is to bring together researchers and practitioners dealing with the general field of information security, with a particular emphasis on the use of signal processing tools in adversarial environments. As such, it addresses all works whereby security is achieved through a combination of techniques from cryptography, computer security, machine learning and multimedia signal processing. Application domains lie, for example, in secure storage, retrieval and tracking of multimedia data, secure outsourcing of computations, forgery detection of multimedia data, or secure use of biometrics. The journal also welcomes survey papers that give the reader a gentle introduction to one of the topics covered as well as papers that report large-scale experimental evaluations of existing techniques. Pure cryptographic papers are outside the scope of the journal. Topics relevant to the journal include, but are not limited to: • Multimedia security primitives (such digital watermarking, perceptual hashing, multimedia authentictaion) • Steganography and Steganalysis • Fingerprinting and traitor tracing • Joint signal processing and encryption, signal processing in the encrypted domain, applied cryptography • Biometrics (fusion, multimodal biometrics, protocols, security issues) • Digital forensics • Multimedia signal processing approaches tailored towards adversarial environments • Machine learning in adversarial environments • Digital Rights Management • Network security (such as physical layer security, intrusion detection) • Hardware security, Physical Unclonable Functions • Privacy-Enhancing Technologies for multimedia data • Private data analysis, security in outsourced computations, cloud privacy
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