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Full Description
Physical Biometrics for Hardware Security of DSP and Machine Learning Coprocessors presents state-of-the art explanations for detective control-based security and protection of digital signal processing (DSP) and machine learning coprocessors against hardware threats. Such threats include intellectual property (IP) abuse and misuse, for example, fraudulent claims of IP ownership and IP piracy. DSP coprocessors such as finite impulse response filters, image processing filters, discrete Fourier transform, and JPEG compression hardware are extensively utilized in several real-life applications. Further, machine learning coprocessors such as convolutional neural network (CNN) hardware IP cores play a vital role in several applications such as face recognition, medical imaging, autonomous driving, and biometric authentication, amongst others.
Written by an expert in the field, this book reviews recent advances in hardware security and IP protection of digital signal processing (DSP) and machine learning coprocessors using physical biometrics and DNA. It presents solutions for secured coprocessors for DSP, image processing applications and CNN, and where relevant chapters explores the advantages, disadvantages and security-cost trade-offs between different approaches and techniques to assist in the practical application of these methods.
The interdisciplinary themes and topics covered are expected to be of interest to researchers in several areas of specialisation, encompassing the overlapping fields of hardware design security, VLSI design (high level synthesis, register transfer level, gate level synthesis), IP core, optimization using evolutionary computing, system-on-chip design, and biometrics. CAD/design engineers, system architects and students will also find this book to be a useful resource.
Contents
Chapter 1: Introduction: secured co-processors for machine learning and DSP applications using biometrics
Chapter 2: Integrated defense using structural obfuscation and encrypted DNA-based biometric for hardware security
Chapter 3: Facial signature-based biometrics for hardware security and IP core protection
Chapter 4: Secured convolutional layer hardware co-processor in convolutional neural network (CNN) using facial biometric
Chapter 5: Handling symmetrical IP core protection and IP protection (IPP) of Trojan-secured designs in HLS using physical biometrics
Chapter 6: Palmprint biometrics vs. fingerprint biometrics vs. digital signature using encrypted hash: qualitative and quantitative comparison for security of DSP coprocessors
Chapter 7: Secured design flow using palmprint biometrics, steganography, and PSO for DSP coprocessors
Chapter 8: Methodology for exploration of security-design cost trade-off for signature-based security algorithms
Chapter 9: Taxonomy of hardware security methodologies: IP core protection and obfuscation
