SHIELD-PCB: securing hardware inspection with enhanced learning and defense against adversarial examples in printed circuit boards images

This study investigates the enhancement of computer vision resilience in intelligent PCB inspection through advanced adversarial example filtration. PCBs are crucial in electronics and require reliable inspection. Current computer vision models are susceptible to adversarial attacks, which compromise their accuracy. Our approach combines deep learning with adversarial training, allowing the model to adapt to potential threats. A refined filtration mechanism mitigates the impact of adversarial attacks during real-time inspections, yielding promising results. This research emphasizes the ongoing necessity of investigating adversarial example filtration to strengthen intelligent inspection systems.