An Autoencoding Method for Detecting Counterfeit Coins

Abstract

We use coins in our daily life to pay for bus, metro tickets, vending machines, etc. However, the market for antique and historical coins is another place, where the quality of coins and their genuinity play a significant role. Hence, researchers have considered different methods in coin detection studies. In recent years 2-D and 3-D image processing approaches have been widely used in image-based coin detection. In this paper, we propose a method to detect counterfeit coins based on image content. We employed SIFT, SURF, and MSER to determine the similarity degree of our datasets. Then, we evaluate those descriptors by statistical analysis to see which one is the most effective criterion for counterfeit coin detection. According to experiments, SIFT was selected as the most reliable algorithm for the Danish coin image dataset. Then, we train an autoencoder to find anomalies in the coin images. The trained autoencoder receives a coin image as input and generates a new image. The output image is compared with a basic image using the selected criterion. If the similarity between these two images meets a threshold then the coin is genuine. Most counterfeit coin detection methods require fake data for training. This can be eliminated by our autoencoding-based anomaly method.