Hardware Trust and Assurance in Image Analysis and Machine Learning Perspectives

The complex long life of the electronic devices coupled with their diverse applications are making them increasingly vulnerable to various forms of threats. Large industry and government efforts are put in place across the globe to address the supply chain security problem and to offer solutions, training, and services. The number of programs introduced by governments have increased over the years in this area.

Although much focus is given to digital domain and testing, physical inspection, image analysis and machine learning for hardware trust and assurance is gaining a lot of attention recently.

Multi-disciplinary research and multi-pronged approaches are sought for development of a fully operational attack targeting different phases in the entire life cycle of an electronic system. Artificial intelligence (AI) and computer vision (CV) play a crucial role in design and development of both threats and countermeasures in hardware. Within the scope of hardware security there a number of areas that require extensive research including physical inspection, decomposition, hardware Trojan design and detection, side-channel analysis, security of Internet of Things (IoT), and secure massive data communication and processing.

Human-level performance of the intelligent and complex tasks, brings in its own pros and cons. The challenges and potential solutions, encourage the researchers to investigate hardware-level vulnerabilities of AI (i.e. implementation attacks, model stealing, etc.), the hardware-level trustworthiness of AI (i.e. robust AI hardware accelerator, implementation attack resilient AI chip design, countermeasures), and the applications of AI for hardware security. In particular, if failure analysis and imaging tools such as voltage imaging or contactless probing techniques are used to provide side information for inspection or attack. In addition, physical fingerprinting based on analog parameters are rapidly providing opportunities for unique countermeasures.

• Emerging topics in physical inspection and assurance
• Counterfeit Detection and Anti-Counterfeit Technique
• Image analysis and artificial intelligence for assurance and inspection
• Chip and PCB level decomposition for assurance
• Electro-optical probing using PEM, EOP, EOFM, etc.
• Hardware Trojan Models and Detection Techniques
• Trusted and Malicious Hardware-Level AI systems
• Cross-Layer Hardware Security
• Security Verification of Intellectual Properties and Integrated Circuits
• Computer Vision for Hardware Security
• Robustness in Hardware-Level Computer Vision Algorithms
• Hardware Implementation of Biometrics, Forensics, and Physical Security Systems
• Fault Injection and Mitigation for Hardware-Level AI Systems

Dr. Navid Asadi
Guest Editor

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