A Machine Learning-Based Digital Twin for Anti-Counterfeiting Applications With Copy Detection Patterns.

In this paper, we present a new approach to model a printing-imaging channel using a machine learning-based “digital twin” for copy detection patterns (CDP). The CDP are considered as modern anti-counterfeiting features in multiple applications. Our digital twin is formulated within the information-theoretic framework of TURBO initially developed for high energy physics simulations, using variational approximations of mutual information for both encoder and decoder in the bidirectional exchange of information. This model extends various architectural designs, including paired pix2pix and unpaired CycleGAN, for image-to-image translation. Applicable to any type of printing and imaging devices, the model needs only training data comprising digital templates sent to a printing device and data acquired by an imaging device. The data can be paired, unpaired, or hybrid, ensuring architectural flexibility and scalability for multiple practical setups. We explore the influence of various architectural factors, metrics, and discriminators on the overall system’s performance in generating and predicting printed CDP from their digital versions and vice versa. We also performed a comparison with several state-of-the-art methods for image-to-image translation applications. The simulation code and extended results are publicly available at https://gitlab.unige.ch/sip-group/digital-twin .