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  • 标题:On the information leakage quantification of camera fingerprint estimates
  • 本地全文:下载
  • 作者:Samuel Fernández-Menduiña ; Fernando Pérez-González
  • 期刊名称:EURASIP Journal on Information Security
  • 印刷版ISSN:1687-4161
  • 电子版ISSN:1687-417X
  • 出版年度:2021
  • 卷号:2021
  • 期号:1
  • 页码:1
  • DOI:10.1186/s13635-021-00121-6
  • 出版社:Hindawi Publishing Corporation
  • 摘要: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.
  • 关键词:Fingerprint ; PRNU ; Leakage ; Information theory ; Membership inference
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