From Quality Inspection to Hardware Security Verification
Abstract
The integrity of electronic assemblies is increasingly threatened by counterfeit components, unauthorized modifications, malicious substitutions, and hardware-based cyberattacks. Traditional mitigation strategies rely primarily on supply-chain documentation, sample-based testing, regulatory compliance, and post-deployment security controls. These approaches provide limited visibility into the actual hardware incorporated into electronic systems.
This paper presents an AI-driven inspection framework that transforms manufacturing inspection infrastructure into a hardware security verification platform. The proposed approach combines bottom-side component inspection during placement with top-side inspection of assembled
printed circuit boards to identify hardware tampering events ןncluding counterfeit devices, remarked components, unauthorized programming, component substitutions, unexpected additions, and assembly modifications.
The framework leverages large-scale manufacturing imagery and deep-learning models trained on billions of component observations collected from production environments. By
correlating visual observations with manufacturing context including approved vendor lists, traceability records, and engineering definitions, the system generates evidence describing component authenticity, provenance, compliance, and assembly integrity.
Industrial examples demonstrate detection of multiple classes of hardware threats directly from production imagery. The results suggest that existing manufacturing inspection infrastructure can serve not only as a quality control mechanism but also as a practical cybersecurity control capable of identifying compromised hardware before deployment.
