CT-ISG: BIRT - Biometric Identification Red Team

CT-ISG：BIRT - 生物识别红队

• 批准号: 0716552
• 负责人: Jonathan Smith
• 金额: $ 35万
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-01 至 2012-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0716552&HistoricalAwards=false
• 关键词: CT; ISG; BIRT; Biometric; Identification

Summary Statement, Biometric Identification Red Team (BIRT)The BIRT methodology will aid biometric system designers in making effective refinements in their systems. The measurement of biological characteristics (biometrics) such as fingerprints and facial images provides a means of identification that neither needs to be carried nor remembered. Evaluation of biometrics has traditionally been focused on the ability of biometric systems to identify members from a population, e.g., for purposes of authentication. As these systems come into more widespread use, attempts will naturally be made to test and frustrate their ability to identify individuals. Understanding these attempts requires a fundamental new analytic approach, based on modeling the capabilities of an adversary with full generality. BIRT develops the adversary model using the information controlled by the adversary, e.g., for recognition, the clothing, glasses and makeup they wear. BIRT uses disinformation theory to abstractly model the adversary capabilities to mask their identity from an interested observer. Disinformation theory is inspired by Shannon's information theoretic model for communications systems, but views the ?noise source? as controlled by the adversary, abstractly modeling the capacity of the adversary to control the noise in the channel (for example, by transforming the image ?signal?) between the biometric sender being identified and the biometric system receiving the identifying information. Face recognition systems will be used to gain experience with and refine the disinformation theory models, with a variety of disguises used as disinformation sources.

摘要声明，生物识别红队（BIRT）的BIRT方法将帮助生物识别系统设计人员在他们的系统进行有效的改进。对指纹和面部图像等生物特征（生物特征）的测量提供了一种既不需要携带也不需要记住的身份识别手段。生物测定的评估传统上集中于生物测定系统从群体中识别成员的能力，例如，用于认证的目的。随着这些系统得到更广泛的使用，自然会尝试测试和挫败它们识别个人的能力。理解这些尝试需要一种基本的新分析方法，该方法基于对对手能力的全面建模。BIRT使用对手控制的信息开发对手模型，例如，他们的衣着、眼镜和化妆都是为了获得认可。BIRT使用虚假信息理论来抽象建模对手的能力，以掩盖他们的身份从一个感兴趣的观察者。虚假信息理论的灵感来自香农的信息理论模型的通信系统，但认为？噪声源？ 由对手控制，抽象建模对手控制信道中噪声的能力（例如，通过变换图像？信号？）在所述生物测定发送者被识别与所述生物测定系统接收所述识别信息之间。人脸识别系统将被用来获得经验和完善虚假信息理论模型，各种伪装被用作虚假信息来源。