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Exploiting Retina Biometric Fused with Encoded Hash for Designing Watermarked Convolutional Hardware IP Against Piracy

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Abstract

The convolution layer in a convolutional neural network (CNN) is highly computationally intensive. It is crucial to design reusable low-cost hardware IP for convolutional layer for enabling hardware-based feature extraction. However, the involvement of fake IP vendor/untrustworthy broker in the integrated circuit (IC) supply chain, makes these IPs susceptible to the threat of piracy. The proposed approach presents high- level synthesis (HLS) driven watermarking methodology for designing low-cost and secure convolutional hardware IP. The presented watermarking approach employs complier-driven high-level transformation and exploits retinal signature fused with the encoded hash for piracy detective countermeasure. The proposed approach, therefore, firstly performs compiler-driven high-level transformation in order to optimize the design latency, followed by embedding the watermark of an authentic IP vendor. The generated watermark in the form of encoded hardware watermarking constraints (digital evidence) is covertly embedded into the resulting optimized design during the register allocation module of HLS. The proposed approach achieves the following: (i) optimized and secure design for convolutional hardware IP, (ii) robust detection of pirated IP at zero design cost overhead, (iii) significantly lower probability of coincidence (in the range of 1.3E−06 to 1.2E−09) indicating stronger digital evidence and higher tamper tolerance (in the range of 2.64E+460 to 9.60E+698) than recent approaches.

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Availability of Data and Material

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work is technically and financially supported in part by the CSIR sanction number 22/0856/23/EMR-II, under CSIR EMR II scheme and Indian Institute of Technology Indore.

Funding

This work is technically and financially supported by CSIR grant no. 22/0856/23/EMR-II.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Information Technology Bhopal, Bhopal, India

    Rahul Chaurasia

  2. Department of Computer Science and Engineering, Indian Institute of Technology, Indore, India

    Anirban Sengupta

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Contributions

Rahul Chaurasia: Development and implementation of the idea, Writing the research paper. Anirban Sengupta: Problem formulation, Ideation, Technical supervision.

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Correspondence to Rahul Chaurasia.

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This work has been submitted on June 1, 2024. This work was supported by Indian Institute of Technology Indore and CSIR.

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Chaurasia, R., Sengupta, A. Exploiting Retina Biometric Fused with Encoded Hash for Designing Watermarked Convolutional Hardware IP Against Piracy. SN COMPUT. SCI. 5, 982 (2024). https://doi.org/10.1007/s42979-024-03247-9

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