Mobile authentication of copy detection patterns

TL;DR

This paper investigates the security and authentication of copy detection patterns (CDP) using machine learning, demonstrating that modern ML methods and mobile phones can reliably detect counterfeits under real-world conditions.

Contribution

It provides a comprehensive analysis of CDP security against illegal copying and proposes machine learning-based authentication methods suitable for mobile verification.

Findings

ML approaches effectively distinguish fake from genuine CDP

Mobile phones can reliably authenticate CDP in real-life conditions

Modern printers and phones enable practical anti-counterfeiting solutions

Abstract

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.