论文标题
在机器学习攻击下对复制检测模式的身份验证：一种监督方法
Authentication of Copy Detection Patterns under Machine Learning Attacks: A Supervised Approach
论文作者
论文摘要
复制检测模式（CDP）是一项有吸引力的技术，可让制造商捍卫其产品免受伪造。 CDP保护机制背后的主要假设是，由于数据处理不平等，这些代码在工业打印机上打印了最小的符号大小（1x1）（1x1）。但是，以前的作品表明，基于机器的攻击可以产生高质量的假货，从而基于传统的基于功能的身份验证系统的身份验证准确性降低。虽然深度学习（DL）可以用作身份验证系统的一部分，但据我们所知，以前的作品都没有研究基于DL的身份验证系统，反对具有1x1符号大小的基于ML的攻击。在这项工作中，我们研究了假设有监督学习（SL）设置的表现。
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.
