Counterfeit gold identification using sound and image processing

Can, Yekta Said; Alagöz, Fatíh; Özer, Emre; Gündebahar, Mücahit

doi:10.1109/siu.2015.7130019

Cited by 2 publications

(7 citation statements)

References 2 publications

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“…Pictures from sunlight, fluorescent light and dim light environments were taken. High accuracies could not be obtained from direct sunlight and direct fluorescent light environments because mika reflects light and shines (Can et al, 2015). In the dim light, I experimented on 30 gold in mika and 30 copper in mika pictures.…”

Section: Image Classificationmentioning

confidence: 99%

“…For each picture, three features were employed. K-fold cross validation was applied to ensure that our system performs high accuracies as stated in (Can et al, 2015). SVM, decision Tree, NN algorithms were used, and they are compared with the CNN algorithm.…”

Section: Image Processing Modulementioning

confidence: 99%

“…The decision tree discriminates two signals by looking at only the B component, as in Figure 7. Bare (Can et al, 2015).…”

Section: Image Processing Modulementioning

confidence: 99%

“…In the banking sector, it is critical to know whether the delivered matter is counterfeit or not upon delivery (Can et al, 2015). At this point, the ability to sell the gold bought from a bank at any point would be a great convenience for a customer.…”

Section: Introductionmentioning

confidence: 99%

“…This situation brings customers difficulty to go to specific branches at specific times. Furthermore, the possibility of human errors and the cost of an expert can be listed as the drawbacks of the current system (Can et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Yapay Sinir Ağları ve Destek Vektör Makineleri Kullanılarak Gerçek ve Sahte Altın Sınıflandırılması

Can¹

2022

Uludağ University Journal of the Faculty of Engineering

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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.

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Section: Image Classificationmentioning

confidence: 99%

Section: Image Processing Modulementioning

confidence: 99%

“…The decision tree discriminates two signals by looking at only the B component, as in Figure 7. Bare (Can et al, 2015).…”

Section: Image Processing Modulementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Yapay Sinir Ağları ve Destek Vektör Makineleri Kullanılarak Gerçek ve Sahte Altın Sınıflandırılması

Can¹

2022

Uludağ University Journal of the Faculty of Engineering

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Fraud Detection in the Marketplace

2024

Advances in Web Technologies and Engineering

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The advent of blockchain technology has revolutionized various industries by introducing decentralized systems with enhanced security and transparency. With the emergence of Web 3.0, blockchain-based marketplaces have gained prominence, facilitating peer-to-peer transactions and eliminating the need for intermediaries. However, these platforms are not immune to fraudulent activities, as malicious actors exploit vulnerabilities in the system. This chapter presents an overview of fraud detection mechanisms in marketplace blockchain Web 3.0. The primary focus is to explore the challenges associated with detecting and preventing fraud in decentralized marketplaces, as well as potential solutions leveraging blockchain technology.

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Counterfeit gold identification using sound and image processing

Cited by 2 publications

References 2 publications

Yapay Sinir Ağları ve Destek Vektör Makineleri Kullanılarak Gerçek ve Sahte Altın Sınıflandırılması

Yapay Sinir Ağları ve Destek Vektör Makineleri Kullanılarak Gerçek ve Sahte Altın Sınıflandırılması

Fraud Detection in the Marketplace

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