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Analysis and experimental evaluation of image-based PUFs

机译:基于图像的PUF的分析和实验评估

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Physically unclonable functions (PUFs) are becoming popular tools for various applications, such as anti-counterfeiting schemes. The security of a PUF-based system relies on the properties of its underlying PUF. Usually, evaluating PUF properties is not simple as it involves assessing a physical phenomenon. A recent work (Armknecht et al. in A formalization of the security features of physical functions. In: IEEE Symposium on Security and Privacy, pp. 397-412, 2011) proposed a generic security framework of physical functions allowing a sound analysis of security properties of PUFs. In this paper, we specialize this generic framework to model a system based on a particular category of PUFs called image-based PUFs. These PUFs are based on random visual features of the physical objects. The model enables a systematic design of the system ingredients and allows for concrete evaluation of its security properties, namely robustness and physical unclonability which are required by anti-counterfeiting systems. As a practical example, the components of the model are instantiated by Laser-Written PUF, White Light Interferometry evaluation, two binary image hashing procedures namely, Random Binary Hashing and Gabor Binary Hashing, respectively, and code-offset fuzzy extraction. We experimentally evaluate security properties of this example for both image hashing methods. Our results show that, for this particular example, adaptive image hashing outperforms the non-adaptive one. The experiments also confirm the usefulness of the formalizations provided by Armknecht et al. (A formalization of the security features of physical functions. In: IEEE Symposium on Security and Privacy, pp. 397-412, 2011) to a practical example. In par ticular, the formalizations provide an asset for evaluating the concrete trade-off between robustness and physical unclonability. To the best of our knowledge, this experimental evaluation of explicit trade-off between robustness and physical unclonability has been performed for the first time in this paper.
机译:物理上不可克隆的功能(PUF)成为各种应用(例如防伪方案)的流行工具。基于PUF的系统的安全性取决于其基础PUF的属性。通常,评估PUF属性并不简单,因为它涉及评估物理现象。最近的一项工作(Armknecht等人在“物理功能的安全性的形式化”中,在:IEEE安全与隐私研讨会,第397-412页,2011年)中提出了物理功能的通用安全框架,可以对安全性进行合理的分析PUF的属性。在本文中,我们专门研究了这种通用框架,以基于特定类别的PUF(称为基于图像的PUF)对系统进行建模。这些PUF基于物理对象的随机视觉特征。该模型可以对系统成分进行系统设计,并可以对其防伪系统的安全特性(即坚固性和物理不可克隆性)进行具体评估。作为一个实际示例,该模型的组件通过激光写入PUF,白光干涉测量评估,两个二进制图像哈希过程(分别是随机二进制哈希和Gabor二进制哈希)以及代码偏移量模糊提取实例化。我们通过实验评估了这两种图像哈希方法的示例安全性。我们的结果表明,对于此特定示例,自适应图像哈希优于非自适应哈希。实验还证实了Armknecht等人提供的形式化的有用性。 (物理功能的安全性的形式化。在:IEEE安全与隐私研讨会,第397-412页,2011年)中举一个实际的例子。特别地,形式化为评估鲁棒性和物理不可克隆性之间的具体折衷提供了资产。据我们所知,本文首次对健壮性和物理不可克隆性之间的显式权衡进行了实验评估。

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