CLASSIFICATION OF ORIGINAL AND COUNTERFEIT GOLD MATTERS BY APPLYING DEEP NEURAL NETWORKS AND SUPPORT VECTOR MACHINES

Abstract

Gold is one of the most counterfeited precious metals. The color of copper is like gold. For this reason, copper is one of the most used materials for color counterfeiting. When the chemical properties are concerned, wolfram is like gold (density of gold and tungsten are 19.30 g/ml and 19.25 g/ml, respectively), so it can be used as a chemical counterfeit. The purity of gold can be determined by X-ray, but this method is costly. The current low-cost methods of jewelers have been experimented with for counterfeit gold detection in this paper. When a gold matter is hit by a subject, the sound frequency is higher than the frequency of sound when the same experiment is done with copper. Furthermore, counterfeit gold color is brighter than real ones. The color of gold is unique, and it is called "gold yellow". In this research, by employing sound and image processing, counterfeit and original gold are differentiated. For the image processing part, first a Convolutional Neural Network (CNN)-based toolbox for segmenting the gold material is applied. Then, deep CNNs for differentiating the color of the gold and copper materials are employed. Promising results are achieved with both sound and image processing techniques.

Keywords

• goruntu isleme
• ses isleme
• sahte altın farklılaşması
• destek vektör makineleri
• evrişimli sinir ağı
• görüntü bölümlendirme
• image processing
• sound processing
• counterfeit gold differentiation
• support vector machines
• convolutional neural network
• image segmentation