Due to development and broad availability of high-quality printing and scanning devices, the number of counterfeited products and documents is dramatically increasing. Therefore, different security elements have been suggested to prevent this socioeconomic plague. One of the most promising and cheap solutions is the use of Copy Detection Pattern (CDP), a maximum entropy image, generated using a secret key. This pattern takes full advantage of information loss principle during printing-and-digitization process to detect copies. Such an unpredictable pattern is highly sensitive to distortions occurring inevitably during production (printing), verification (digitization) and reproduction (duplication) processes. Initially, the detection of counterfeited CDP was devoted to evaluating the level of information loss using Pearson correlation. However, the security of CDP based authentication system was shown to be vulnerable to estimation attacks based on neural network that can infer a CDP after scanning. In this paper, we study how to increase the performance of a detector using a similarity metric learning approach.