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

Authors: Brian Pulfer, Roman Chaban, Yury Belousov, Joakim Tutt, Olga Taran, Taras Holotyak, Slava Voloshynovskiy
Published: 2022-06-23 | Updated: 2022-06-25

Source: https://arxiv.org/abs/2206.11793

PDF: https://arxiv.org/pdf/2206.11793

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アルゴリズム設計 自己教師あり学習 深層学習技術

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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 (1×1) 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 1×1 symbol size. In this work, we study such a performance assuming a supervised learning (SL) setting.