Counterfeit Money Detection: A Hybrid Semi-Supervised GAN-based Approach

Despite recent advances in digital banking around the world, the use of banknotes is still predominant. In this context, the detection of fake money bills is important due to the severe impact of their circulation on the economic system. Automating and making detecting counterfeited bills accessible to anyone is still an under-investigated issue, which is mainly based on the visual inspection of bills. In this paper, we propose a novel semi-supervised generative adversarial network (GAN)-based detection approach that, by visually inspecting a bill, can determine if the latter is authentic or fake. Our method originally combines a parallelized GAN to a semi-supervised GAN. On a dataset of Tunisian bills, our method is able to achieve 100% accuracy, with a Fréchet Inception Distance (FID) below 1, i.e., generating diversified fake images with good quality. These performances are proven superior to those of several benchmark approaches.