Journal Article10.1109/TVLSI.2020.2999514
Securing Hardware Accelerators for CE Systems Using Biometric Fingerprinting
Anirban Sengupta,Mahendra Rathor +1 more
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TL;DR: A novel methodology to secure hardware accelerators against ownership threats/IP piracy using biometric fingerprinting, followed by embedding fingerprint’s digital template into the design in the form of secret biometric constraints; thereby generating a secured hardware accelerator design.
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Abstract: This article presents a novel methodology to secure hardware accelerators (such as digital signal processing (DSP) and multimedia intellectual property (IP) cores) against ownership threats/IP piracy using biometric fingerprinting. In this approach, an IP vendor’s biometric fingerprint is first converted into a corresponding digital template, followed by embedding fingerprint’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 11 different fingerprints on probability of coincidence (Pc) metric. As evident, the proposed approach achieves a very low Pc value in the range of 2.22E−3 to 4.35E−6. Further, the biometric fingerprint achieves total constraints size between minimum 350 bits to maximum 895 bits; 2) impact of six different resource constraints on the design cost overhead of JPEG compression hardware postembedding biometric fingerprint. As evident, for all the resource constraints implemented, the design cost overhead is 0%; and 3) comparative analysis of proposed biometric fingerprint with recent work, for five different signature strength values, in terms of Pc. As evident, the proposed approach achieves minimum 3.9E+2 times and maximum 6.9E+4 times lower Pc, when compared to recent work.
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Citations
Finger Vein Recognition Algorithm Based on Lightweight Deep Convolutional Neural Network
Jiaquan Shen,Ningzhong Liu,Cheng Xu,Han Sun,Yushun Xiao,Deguang Li,Yongxin Zhang +6 more
TL;DR: Wang et al. as mentioned in this paper used a lightweight convolutional model in the backbone network and employed a triplet loss function to train the model, which not only improves the matching accuracy, but also satisfies the real-time matching requirements.
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A Robust Biometric Authentication System for Handheld Electronic Devices by Intelligently Combining 3D Finger Motions and Cerebral Responses
TL;DR: In this article, a new method is proposed to verify air signatures by analyzing finger movements and cerebral activities together with the help of sensors in next-generation consumer electronic (CE) devices.
23
Facial Biometric for Securing Hardware Accelerators
Anirban Sengupta,Mahendra Rathor +1 more
TL;DR: A novel facial biometrics-based hardware security methodology to secure hardware accelerators against ownership threats/IP piracy by embedding facial signature's digital template into the design in the form of secret biometric constraints, thereby generating a secured hardware accelerator design.
21
Contact-Less Palmprint Biometric for Securing DSP Coprocessors Used in CE Systems
TL;DR: In this paper, a novel contactless palmprint biometric hardware security approach for securing DSP-based coprocessors is presented, which is capable of generating secret biometric palmprint constraints that are embedded in DSP designs used in consumer electronics systems to detect counterfeited versions.
18
Robust Security of Hardware Accelerators Using Protein Molecular Biometric Signature and Facial Biometric Encryption Key
S. Sengupta,Rahul K. Chaurasia,Aditya Anshul +2 more
- 01 Jun 2023
TL;DR: In this paper , a robust encrypted protein molecular biometric signature-based hardware security approach was proposed to secure hardware accelerators (like digital signal processing (DSP) and multimedia intellectual property (IP) cores) against threats of piracy and ownership abuse.
6
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