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.

Copy Detection Pattern (CDP) technology is a promising anti-counterfeiting solution for the protection of physical goods. In recent years, it has been shown that this technology is threatened by powerful deep learning attacks that are able to bypass original authentication schemes. In this paper, we tackle this problem by proposing a new CDP authentication scheme based on statistical knowledge discovered about the printing and imaging process. The novelty of our approach lies in providing means to measure the reliability of each local pattern appearing in the CDP. This allows to define new authentication measures to better differentiate original CDP from fakes. Our results show that this new system is capable of performing reliable CDP authentication with smartphones without the need for heavyweight machine learning tools requiring massive data entries.

In the recent years, the copy detection patterns (CDP) attracted a lot of attention as a link between the physical and digital worlds, which is of great interest for the internet of things and brand protection applications. However, the security of CDP in terms of their reproducibility by unauthorized parties or clonability remains largely unexplored. In this respect, this paper addresses a problem of anti-counterfeiting of physical objects and aims at investigating the authentication aspects and the resistances to illegal copying of the modern CDP from machine learning perspectives. A special attention is paid to a reliable authentication under the real-life verification conditions when the codes are printed on an industrial printer and enrolled via modern mobile phones under regular light conditions. The theoretical and empirical investigation of authentication aspects of CDP is performed with respect to four types of copy fakes from the point of view of (i) multi-class supervised classification as a baseline approach and (ii) one-class classification as a real-life application case. The obtained results show that the modern machine-learning approaches and the technical capacities of modern mobile phones allow to reliably authenticate CDP on end-user mobile phones under the considered classes of fakes.

Copy detection patterns (CDP) are an attractive technology that allows manufacturers to defend their products against counterfeiting. The main assumption behind the protection mechanism of CDP is that these codes printed with the smallest symbol size (1x1) on an industrial printer cannot be copied or cloned with sufficient accuracy due to data processing inequality. However, previous works have shown that Machine Learning (ML) based attacks can produce high-quality fakes, resulting in decreased accuracy of authentication based on traditional feature-based authentication systems. While Deep Learning (DL) can be used as a part of the authentication system, to the best of our knowledge, none of the previous works has studied the performance of a DL-based authentication system against ML-based attacks on CDP with 1x1 symbol size. In this work, we study such a performance assuming a supervised learning (SL) setting.