On the information leakage quantification of camera fingerprint estimates

IF 2.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

EURASIP Journal on Information Security Pub Date : 2021-06-03 DOI:10.1186/s13635-021-00121-6

Samuel Fernández-Menduiña, Fernando Pérez-González

引用次数: 4

Abstract

Camera fingerprints based on sensor PhotoResponse Non-Uniformity (PRNU) have gained broad popularity in forensic applications due to their ability to univocally identify the camera that captured a certain image. The fingerprint of a given sensor is extracted through some estimation method that requires a few images known to be taken with such sensor. In this paper, we show that the fingerprints extracted in this way leak a considerable amount of information from those images used in the estimation, thus constituting a potential threat to privacy. We propose to quantify the leakage via two measures: one based on the Mutual Information, and another based on the output of a membership inference test. Experiments with practical fingerprint estimators on a real-world image dataset confirm the validity of our measures and highlight the seriousness of the leakage and the importance of implementing techniques to mitigate it. Some of these techniques are presented and briefly discussed.

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相机指纹估计的信息泄漏量化研究

基于传感器光响应非均匀性(PRNU)的相机指纹在法医应用中获得了广泛的普及，因为它们能够唯一地识别拍摄特定图像的相机。通过某种估计方法提取给定传感器的指纹，该估计方法需要使用该传感器拍摄少量已知图像。在本文中，我们证明了以这种方式提取的指纹泄露了用于估计的图像中的大量信息，从而构成了对隐私的潜在威胁。我们建议通过两个度量来量化泄漏:一个基于互信息，另一个基于隶属推理测试的输出。在真实图像数据集上使用实际指纹估计器进行的实验证实了我们的措施的有效性，并强调了泄漏的严重性以及实施减轻泄漏的技术的重要性。本文介绍并简要讨论了其中的一些技术。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

EURASIP Journal on Information Security COMPUTER SCIENCE, INFORMATION SYSTEMS-

CiteScore

8.80

自引率

0.00%

发文量

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