  In this paper, we address the problem of modeling a printing-imaging channel
built on a machine learning approach a.k.a. digital twin for
anti-counterfeiting applications based on copy detection patterns (CDP). The
digital twin is formulated on an information-theoretic framework called Turbo
that uses variational approximations of mutual information developed for both
encoder and decoder in a two-directional information passage. The proposed
model generalizes several state-of-the-art architectures such as adversarial
autoencoder (AAE), CycleGAN and adversarial latent space autoencoder (ALAE).
This model can be applied to any type of printing and imaging and it only
requires training data consisting of digital templates or artworks that are
sent to a printing device and data acquired by an imaging device. Moreover,
these data can be paired, unpaired or hybrid paired-unpaired which makes the
proposed architecture very flexible and scalable to many practical setups. We
demonstrate the impact of various architectural factors, metrics and
discriminators on the overall system performance in the task of
generation/prediction of printed CDP from their digital counterparts and vice
versa. We also compare the proposed system with several state-of-the-art
methods used for image-to-image translation applications.
