当前位置: X-MOL 学术IEEE Trans. Very Larg. Scale Integr. Syst. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Facial Biometric for Securing Hardware Accelerators
IEEE Transactions on Very Large Scale Integration (VLSI) Systems ( IF 2.8 ) Pub Date : 2020-10-21 , DOI: 10.1109/tvlsi.2020.3029245
Anirban Sengupta , Mahendra Rathor

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.

中文翻译:

面部生物识别技术,用于保护硬件加速器

本文提出了一种新颖的基于面部生物特征的硬件安全方法,以保护硬件加速器(例如数字信号处理(DSP)和多媒体知识产权(IP)内核)免受所有权威胁/ IP盗版的侵害。在这种方法中,首先将IP供应商的面部生物特征转换为代表数字模板的相应面部特征,然后以秘密生物特征约束的形式将面部特征的数字模板嵌入到设计中,从而生成安全的硬件加速器设计。结果报告了对所提出的生物特征指纹方法的以下定性和定量分析:1)五个不同的面部生物特征约束对重合概率(Pc)度量(指示数字证据的强度)的影响。所提出的方法在1.54E-5至2.01E-5范围内实现了非常低的Pc值。2)面部生物特征图像的不同面部特征集对生成的秘密约束总数和Pc的影响。显而易见,对于所有实施的面部特征集,Pc范围在3.31E–4和2.01E–5之间。和3)就PC而言,针对不同的DSP应用和五种不同的面部生物特征图像,对所提议方法与最新工作进行了比较分析。显然,与最近的工作相比,所提出的方法可显着降低Pc。和3)就PC而言,针对不同的DSP应用和五种不同的面部生物特征图像,对所提议方法与最新工作进行了比较分析。显然,与最近的工作相比,所提出的方法可显着降低Pc。(3)就PC而言,针对不同的DSP应用和五种不同的面部生物特征图像,与最近的工作进行了比较。显然,与最近的工作相比,所提出的方法可显着降低Pc。
更新日期:2020-10-21
down
wechat
bug