arXiv:2503.15648cs.CRcs.CV2025-03被引 15

用随机变换生成可撤销的生物特征模板,防泄露且难逆推。

Cancelable Biometric Template Generation Using Random Feature Vector Transformations

  • 通过随机分组特征向量生成距离向量作为伪标识。
  • 人脸与指纹识别最差情况下等错误率分别为1.5%和1.7%。
  • 不存储原始特征,抗重建攻击,适用于多模态场景。

可撤销生物特征方案旨在从生物特征数据中提取保持身份、不可逆且可撤销的伪标识符。识别系统仅需存储该伪标识符,以避免在识别过程中原始生物特征数据被篡改或窃取。现有先进方案通过用户特定加盐或一对多变换生成伪标识符。然而,这些方法存在性能问题,且多为特定模态,易受重建攻击,因安全关键的变换密钥可能被非法获取。本文提出一种新型、模态无关的可撤销生物特征方案:将多个随机变换后的特征向量间的距离构成距离向量作为可撤销模板。该变换基于用户特定的随机向量对特征向量分组完成。所提方案消除模板重构可能性,因生成的可撤销模板仅包含不同随机变换间距离值,不保存原始模板任何细节。在人脸与指纹模态上评估了该方案的识别性能,最坏情况下人脸识别等错误率为1.5%,指纹识别为1.7%。

原文摘要 · Abstract (English)

Cancelable biometric schemes are designed to extract an identity-preserving, non-invertible as well as revocable pseudo-identifier from biometric data. Recognition systems need to store only this pseudo-identifier, to avoid tampering and/or stealing of original biometric data during the recognition process. State-of-the-art cancelable schemes generate pseudo-identifiers by transforming the original template using either user-specific salting or many-to-one transformations. In addition to the performance concerns, most of such schemes are modality-specific and prone to reconstruction attacks as there are chances for unauthorized access to security-critical transformation keys. A novel, modality-independent cancelable biometric scheme is proposed to overcome these limitations. In this scheme, a cancelable template (pseudo identifier) is generated as a distance vector between multiple random transformations of the biometric feature vector. These transformations were done by grouping feature vector components based on a set of user-specific random vectors. The proposed scheme nullifies the possibility of template reconstruction as the generated cancelable template contains only the distance values between the different random transformations of the feature vector and it does not store any details of the biometric template. The recognition performance of the proposed scheme is evaluated for face and fingerprint modalities. Equal Error Rate (EER) of 1.5 is obtained for face and 1.7 is obtained for the fingerprint in the worst case.

生物特征可撤销隐私保护安全识别

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