Authentication of Copy Detection Patterns under Machine Learning Attacks: A Supervised Approach

TL;DR

This paper evaluates the robustness of copy detection pattern authentication systems against machine learning attacks, focusing on deep learning methods and supervised learning scenarios to identify vulnerabilities.

Contribution

It is the first study to analyze the performance of deep learning-based authentication systems against ML-based attacks on 1x1 symbol size copy detection patterns.

Findings

Deep learning can be exploited to forge CDP with high accuracy.

Traditional authentication methods are vulnerable to ML-based attacks.

Supervised learning approaches reveal significant weaknesses in current CDP defenses.

Abstract

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.