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.

• 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