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Facial Biometric for Securing Hardware Accelerators

机译:保护硬件加速器的面部生物识别

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This article presents a novel facial biometrics-based hardware security methodology to secure hardware accelerators [such as digital signal processing (DSP) and multimedia intellectual property (IP) cores] against ownership threats/IP piracy. In this approach, an IP vendor’s facial biometrics is first converted into a corresponding facial signature representing digital template, followed by embedding facial signature’s digital template into the design in the form of secret biometric constraints, thereby generating a secured hardware accelerator design. The results report the following qualitative and quantitative analysis of the proposed biometric fingerprint approach: 1) impact of five different facial biometrics constraints on probability of coincidence (Pc) metric (indicating strength of digital evidence). The proposed approach achieves a very low Pc value in the range of 1.54E–5 to 2.01E–5; 2) impact of different facial feature set of a facial biometric image on total number of generated secret constraints and Pc. As evident, for all facial feature sets implemented, Pc ranges between 3.31E–4 and 2.01E–5; and 3) comparative analysis of proposed approach with recent work, for different DSP applications and five different facial biometric images, in terms of Pc. As evident, the proposed approach achieves significantly lower Pc, compared with recent work.
机译:本文提出了一种基于新的面部生物识别的硬件安全方法,可以保护硬件加速器[如数字信号处理(DSP)和多媒体知识产权(IP)核心]免受所有权威胁/ IP盗版。在这种方法中,首先将IP供应商的面部生物测量学转换为代表数字模板的相应面部签名,然后以秘密生物识别约束的形式将面部签名的数字模板嵌入设计中,从而产生安全的硬件加速器设计。结果报告了对所提出的生物识别指纹方法的定性和定量分析:1)五种不同面部生物识别的影响对巧合(PC)度量的概率(表明数字证据的强度)。所提出的方法在1.54E-5至2.01E-5的范围内实现了非常低的PC值; 2)不同面部特征集的面部生物识别图像的影响在生成的秘密约束和PC的总数上。视为明显,对于实现的所有面部特征集,PC范围在3.31e-4和2.01e-5之间; 3)对近期工作的比较分析,不同的DSP应用和五种不同的面部生物识别图像,在PC方面。与最近的工作相比,拟议方法达到了显着降低的PC。

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