DeepMoney: Counterfeit Money Detection Using Generative Adversarial Networks

Conventional paper currency and modern electronic currency are two important modes of transactions. In several parts of the world, conventional methodology has clear precedence over its electronic counterpart. However, the identification of forged currency paper notes is now becoming an increasingly crucial problem because of the new and improved tactics employed by counterfeiters. In this paper, a machine assisted system – dubbed DeepMoney– is proposed which has been developed to discriminate fake notes from genuine ones. For this purpose, state-of-the-art models of machine learning called Generative Adversarial Networks (GANs) are employed. GANs use an unsupervised learning to train a model that can then be used to perform supervised predictions. This flexibility provides the best of both worlds by allowing unlabelled data to be trained on whilst still making concrete predictions. This technique was applied to Pakistani banknotes. State-of-the-art image processing and feature recognition techniques were used to design the overall approach of a valid input. Augmented samples of images were used in the experiments which show that a high-precision machine can be developed to recognize genuine paper money. An accuracy of 80% has been achieved. The code is available as an open source to allow others to reproduce and build upon the efforts already made.<br>

传统纸币与现代电子货币是两类重要的交易媒介。在全球诸多区域，传统纸币的应用仍显著优于电子货币。然而，随着造假者不断升级造假手段，纸币真伪鉴别正逐渐成为愈发关键的问题。本文提出一种名为DeepMoney的机器辅助鉴别系统，用于区分真伪纸币。为此，本文采用了当前最先进的生成式对抗网络（Generative Adversarial Networks，GANs）机器学习模型。生成式对抗网络通过无监督学习完成模型训练，后续可用于执行监督式预测任务，这种灵活性兼顾了无标注数据训练与精准预测的双重优势。研究团队采用前沿图像处理与特征识别技术构建了完整的有效输入处理流程，并将本技术应用于巴基斯坦纸币的真伪鉴别任务，实验中使用了图像增强样本，结果证实可开发出高精度的纸币真伪识别系统，最终实现了80%的识别准确率。本研究的代码已开源，以供其他研究者复现研究成果并基于现有工作开展后续拓展研究。