The global chip industry is grappling with dual challenges: a profound shortage of new chips and a surge of counterfeit chips valued at $75 billion, introducing substantial risks of malfunction and unwanted surveillance. To counteract this, we propose an optical anti-counterfeiting detection method for semiconductor devices that is robust under adversarial tampering features, such as malicious package abrasions, compromised thermal treatment, and adversarial tearing. Our new deep-learning approach uses a RAPTOR (residual, attention-based processing of tampered optical response) discriminator, showing the capability of identifying adversarial tampering to an optical, physical unclonable function based on randomly patterned arrays of gold nanoparticles. Using semantic segmentation and labeled clustering, we efficiently extract the positions and radii of the gold nanoparticles in the random patterns from 1000 dark-field images in just 27 ms and verify the authenticity of each pattern using RAPTOR in 80 ms with 97.6% accuracy under difficult adversarial tampering conditions. We demonstrate that RAPTOR outperforms the state-of-the-art Hausdorff, Procrustes, and average Hausdorff distance metrics, achieving a 40.6%, 37.3%, and 6.4% total accuracy increase, respectively.