Mathematical model of printing-imaging channel for blind detection of fake copy detection patterns

TL;DR

This paper introduces a mathematical model of the printing-imaging channel for authenticating copy detection patterns, enabling reliable detection of deep learning generated fakes using only digital references.

Contribution

It presents a novel mathematical model and detection scheme that improve fake copy detection, especially against deep learning generated forgeries, using only digital references.

Findings

Reliable authentication of deep learning generated fakes.

Effective detection using only digital references.

Model enhances anti-counterfeiting measures.

Abstract

Nowadays, copy detection patterns (CDP) appear as a very promising anti-counterfeiting technology for physical object protection. However, the advent of deep learning as a powerful attacking tool has shown that the general authentication schemes are unable to compete and fail against such attacks. In this paper, we propose a new mathematical model of printing-imaging channel for the authentication of CDP together with a new detection scheme based on it. The results show that even deep learning created copy fakes unknown at the training stage can be reliably authenticated based on the proposed approach and using only digital references of CDP during authentication.