Researchers present a novel framework that uses image-mining techniques and machine learning algorithms to identify flaws in counterfeit coins. The researchers’ framework uses fuzzy association rules mining to find patterns that are similar but ‘fuzzy,’ i.e., not clear enough to be exact copies. However, the framework will eventually arrive at a certain range of results where positive matches be confidently identified.to extract frequent patterns from the images. These patterns capture relationships among the blobs’ attributes, such as color, texture, shape and size. The patterns help researchers to better understand the images and tell whether a coin is real or fake.
