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

Yıl 2022, Cilt: 27 Sayı: 1, 89 - 102, 30.04.2022

Yekta Said Can

https://doi.org/10.17482/uumfd.1054013

Öz

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.

Anahtar Kelimeler

image processing sound processing counterfeit gold differentiation support vector machines convolutional neural network image segmentation

Destekleyen Kurum

Tübitak

Proje Numarası

TEYDEB 9150222 Akıllı Altın Tanıma Teknolojisi

Teşekkür

This study was carried out as a part of the E!9874 Smart Magnetic Identification Technology (TEYDEB 9150222 Smart Gold Identification Technology) project within the EU Eurostars Programme. I would like to thank the KuveytTurk Participation Bank RD Center team for their contribution to the project. I would like to also thank Professor Fatih ALAGÖZ for his contributions in this study and for managing the project.

Kaynakça

  • 1. Alpaydin, E. (2021) Introduction to machine learning. MIT Press, Boston, USA.
  • 2. Battaini, P., E. Bemporad, and D. De Felicis (2014) The fire assay reloaded. Gold Bulletin, 47 (1-2): 9-20. doi:10.1007/s13404-013-0101-1
  • 3. Brill, M. and K.H. Wiedemann (1992) Determination of gold in gold jewellery alloys by ICP spectrometry. Gold Bulletin, 25 (1): 13-26. doi: 10.1007/BF03214719
  • 4. Brill, M. Analysis of carat gold (1997) Gold technology, 22 (1): 10.
  • 5. Can, Y. S., Alagoz, F., Özer, E., Gündebahar, M. (2015) Counterfeit gold identification using sound and image processing. 23rd Signal Processing and Communications Applications Conference (SIU), Malatya, Turkey, May 16-19, 2015. pp. 1074-1077. doi: 10.1109/SIU.2015.7130019
  • 6. Caudill, M. (1987) Neural networks primer, part I. AI expert, 2 (12): 46-52.
  • 7. Crain, G., Suarez, R. (2014) U.S. Patent Application No. 14/154,891.
  • 8. Eames, D.A., Eames G. A., Eames M. A., inventors (2015) Device to test and authenticate precious metal objects. United States patent application US 14/661,466. Oct 29.
  • 9. Glorot, X., Bengio, Y. (2010) Understanding the difficulty of training deep feedforward neural networks. Thirteenth international conference on artificial intelligence and statistics, Sardinia, Italy, 13-15 May 2010, pp. 249-256.
  • 10. Hanrahan, J.F. (1962) The fire assay method as applied to high purity gold at the Rand Refinery, Limited. Journal of the Southern African Institute of Mining and Metallurgy, 62 (12): 712-727.
  • 11. He, K., Zhang, X., Ren, S., Sun, J. (2016) Deep residual learning for image recognition. IEEE conference on computer vision and pattern recognition, Las Vegas, NV, USA, 27-30 June 2016, pp. 770–778. doi: 10.1109/CVPR.2016.90
  • 12. Hill, T., Lewicki, P., Lewicki, P. (2006) Statistics: methods and applications: a comprehensive reference for science, industry, and data mining. StatSoft Inc., Oklahoma, OK, USA.
  • 13. Ioffe, S. (2017). Batch renormalization: Towards reducing minibatch dependence in batch-normalized models. arXiv preprint arXiv:1702.03275.
  • 14. Ismail, M. P., Sani, S., bin Mohd Shofri, F. S., Harun, M., Omar, N. B. (2018). Ultrasonic inspection of fake gold jewelry. IOP Conference Series: Materials Science and Engineering (Vol. 298, No. 1, p. 012025). IOP Publishing. doi: 10.1088/1757-899X/298/1/012025
  • 15. Jalas, P., J.P. Ruottinen, and S. Hemminki (2002) XRF analysis of jewelry using fully standardless fundamental parameter approach. Gold technology, 35 (1): 28.
  • 16. Karadjova, I., S. Arpadjan, and L. Jordanova (2000) Determination of trace metals in high purity gold. Fresenius’ Journal of Analytical Chemistry, 367 (2): 146-150. doi: 10.1007/s002160051615
  • 17. Kinneberg, D.J., S.R. Williams, and D.P. Agarwal (1998) Origin and effects of impurities in high purity gold. Gold Bulletin, 31 (2): 58-67. doi: 10.1007/BF03214762
  • 18. Majcen N, Majer M, Svegl IG, De Bievre P. (2002) Traceability of results of measurements of gold in precious metal alloys by XRF. Acta Chimica Slovenica. Jan 1,49(1):187-94.
  • 19. Nor, F. M., Tamuri, A. R., Ismail, A. K. (2019) Fake gold: Gold purity measurement using nondestructive method. International Journal of Engineering Technology, 8 (1), 165-172. doi : 10.13140/RG.2.2.29388.46727
  • 20. Oliveira, S. A., Seguin, B., Kaplan, F. (2018) dhSegment: A generic deep-learning approach for document segmentation. 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), Niagara Falls, NY, USA, 2018, pp. 7-12. doi: 10.1109/ICFHR-2018.2018.00011
  • 21. Piorek, S. (2005) Portable X-ray fluorescence analyzer for the first level screening of materials for prohibited substances. International Conference on Asian Green Electronics AGEC, Shanghai, China, March 15-18, pp. 7-13. doi: 10.1109/AGEC.2005.1452307
  • 22. Piorek S, Shefsky S. I., Dugas M. E., inventors (2013) Thermo Scientific Portable Analytical Instruments Inc, assignee. System and method for identification of counterfeit gold jewelry using xrf. United States patent application US 13/365,713. Aug 8.
  • 23. Rastrelli, A., et al. (2009) Modern and ancient gold jewellery attributed to the Etruscans: a science-based study. ArcheoSciences Revue d’arch´eom´etrie, 1 (33): 357-364. doi: https://doi.org/10.4000/archeosciences.2449
  • 24. Raw, P. (1997) The assaying and refining of gold, a guide for the gold jewellery producer. World Gold Council, 1 (1): 1-10.
  • 25. Ronneberger, O., Fischer, P., Brox,T. (2015) U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical image computing and computer-assisted intervention, Munich, Germany, October 5-9, pp. 234-241. doi: 10.1007/978-3-319-24574-4_28
  • 26. Schaffer, S. (2003) Golden means: Assay instruments and the geography of precision in the Guinea trade, Instruments, Travel and Science, Routledge. p. 32-62.
  • 27. Singh, N. (2012) A rugged, precise and accurate new gravimetry method for the determination of gold: an alternative to fire assay method. SpringerPlus, 1 (1): 1-6. doi: 10.1186/2193-1801-1-14
  • 28. Smrcka, L., K. Misek, and J. Bednar (1965) The density of quenched gold. Cechoslovackij Fiziceskij Zurnal B, 15 (6): 418-424.
  • 29. Specification for Density hydrometers. British Standard 1991. BS718:1991.

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

Yıl 2022, Cilt: 27 Sayı: 1, 89 - 102, 30.04.2022

Yekta Said Can

https://doi.org/10.17482/uumfd.1054013

Öz

Altın, en çok taklit edilen değerli metallerden biridir. Bakırın rengi altına benzer. Bu nedenle bakır, renk sahteciliği için en yaygın kullanılan malzemelerden biridir. Kimyasal özellikler söz konusu olduğunda, volfram altına benzer (altın ve tungstenin yoğunluğu sırasıyla 19.30 g/ml ve 19.25 g/ml'dir), bu nedenle kimyasal bir sahte olarak kullanılabilir. Altının saflığı X-ray ile belirlenebilir, ancak bu yöntem maliyetlidir. Bu yazıda, sahte altın tespiti için kuyumcuların mevcut düşük maliyetli yöntemleri ve sahte parayı tespit etmek için kullanılan düşük maliyetli yöntemler denenmiştir. Bir yüzeye altın bir madde çarptığında, ses frekansı aynı deney bakır ile yapıldığındaki sesin frekansından daha yüksektir. Ayrıca, sahte altın rengi gerçek olanlardan daha parlaktır. Altın rengi benzersizdir ve "altın sarısı" olarak adlandırılır. Bu araştırmada ses ve görüntü işleme yöntemleri kullanılarak sahte ve orijinal altın ayrımı yapılmıştır. Görüntü işleme kısmı için, önce görüntüden altını segmentlere ayırmak için CNN tabanlı bir araç kutusu uygulanır. Bundan sonra, altın ve bakır malzemelerin rengini ayırt etmek için derin Evrişimli Sinir Ağları kullanılır. Hem ses hem de görüntü işleme teknikleri ile umut verici sonuçlar elde edilmektedir.

Anahtar Kelimeler

goruntu isleme ses isleme sahte altın farklılaşması destek vektör makineleri evrişimli sinir ağı görüntü bölümlendirme

Proje Numarası

TEYDEB 9150222 Akıllı Altın Tanıma Teknolojisi

Kaynakça

  • 1. Alpaydin, E. (2021) Introduction to machine learning. MIT Press, Boston, USA.
  • 2. Battaini, P., E. Bemporad, and D. De Felicis (2014) The fire assay reloaded. Gold Bulletin, 47 (1-2): 9-20. doi:10.1007/s13404-013-0101-1
  • 3. Brill, M. and K.H. Wiedemann (1992) Determination of gold in gold jewellery alloys by ICP spectrometry. Gold Bulletin, 25 (1): 13-26. doi: 10.1007/BF03214719
  • 4. Brill, M. Analysis of carat gold (1997) Gold technology, 22 (1): 10.
  • 5. Can, Y. S., Alagoz, F., Özer, E., Gündebahar, M. (2015) Counterfeit gold identification using sound and image processing. 23rd Signal Processing and Communications Applications Conference (SIU), Malatya, Turkey, May 16-19, 2015. pp. 1074-1077. doi: 10.1109/SIU.2015.7130019
  • 6. Caudill, M. (1987) Neural networks primer, part I. AI expert, 2 (12): 46-52.
  • 7. Crain, G., Suarez, R. (2014) U.S. Patent Application No. 14/154,891.
  • 8. Eames, D.A., Eames G. A., Eames M. A., inventors (2015) Device to test and authenticate precious metal objects. United States patent application US 14/661,466. Oct 29.
  • 9. Glorot, X., Bengio, Y. (2010) Understanding the difficulty of training deep feedforward neural networks. Thirteenth international conference on artificial intelligence and statistics, Sardinia, Italy, 13-15 May 2010, pp. 249-256.
  • 10. Hanrahan, J.F. (1962) The fire assay method as applied to high purity gold at the Rand Refinery, Limited. Journal of the Southern African Institute of Mining and Metallurgy, 62 (12): 712-727.
  • 11. He, K., Zhang, X., Ren, S., Sun, J. (2016) Deep residual learning for image recognition. IEEE conference on computer vision and pattern recognition, Las Vegas, NV, USA, 27-30 June 2016, pp. 770–778. doi: 10.1109/CVPR.2016.90
  • 12. Hill, T., Lewicki, P., Lewicki, P. (2006) Statistics: methods and applications: a comprehensive reference for science, industry, and data mining. StatSoft Inc., Oklahoma, OK, USA.
  • 13. Ioffe, S. (2017). Batch renormalization: Towards reducing minibatch dependence in batch-normalized models. arXiv preprint arXiv:1702.03275.
  • 14. Ismail, M. P., Sani, S., bin Mohd Shofri, F. S., Harun, M., Omar, N. B. (2018). Ultrasonic inspection of fake gold jewelry. IOP Conference Series: Materials Science and Engineering (Vol. 298, No. 1, p. 012025). IOP Publishing. doi: 10.1088/1757-899X/298/1/012025
  • 15. Jalas, P., J.P. Ruottinen, and S. Hemminki (2002) XRF analysis of jewelry using fully standardless fundamental parameter approach. Gold technology, 35 (1): 28.
  • 16. Karadjova, I., S. Arpadjan, and L. Jordanova (2000) Determination of trace metals in high purity gold. Fresenius’ Journal of Analytical Chemistry, 367 (2): 146-150. doi: 10.1007/s002160051615
  • 17. Kinneberg, D.J., S.R. Williams, and D.P. Agarwal (1998) Origin and effects of impurities in high purity gold. Gold Bulletin, 31 (2): 58-67. doi: 10.1007/BF03214762
  • 18. Majcen N, Majer M, Svegl IG, De Bievre P. (2002) Traceability of results of measurements of gold in precious metal alloys by XRF. Acta Chimica Slovenica. Jan 1,49(1):187-94.
  • 19. Nor, F. M., Tamuri, A. R., Ismail, A. K. (2019) Fake gold: Gold purity measurement using nondestructive method. International Journal of Engineering Technology, 8 (1), 165-172. doi : 10.13140/RG.2.2.29388.46727
  • 20. Oliveira, S. A., Seguin, B., Kaplan, F. (2018) dhSegment: A generic deep-learning approach for document segmentation. 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), Niagara Falls, NY, USA, 2018, pp. 7-12. doi: 10.1109/ICFHR-2018.2018.00011
  • 21. Piorek, S. (2005) Portable X-ray fluorescence analyzer for the first level screening of materials for prohibited substances. International Conference on Asian Green Electronics AGEC, Shanghai, China, March 15-18, pp. 7-13. doi: 10.1109/AGEC.2005.1452307
  • 22. Piorek S, Shefsky S. I., Dugas M. E., inventors (2013) Thermo Scientific Portable Analytical Instruments Inc, assignee. System and method for identification of counterfeit gold jewelry using xrf. United States patent application US 13/365,713. Aug 8.
  • 23. Rastrelli, A., et al. (2009) Modern and ancient gold jewellery attributed to the Etruscans: a science-based study. ArcheoSciences Revue d’arch´eom´etrie, 1 (33): 357-364. doi: https://doi.org/10.4000/archeosciences.2449
  • 24. Raw, P. (1997) The assaying and refining of gold, a guide for the gold jewellery producer. World Gold Council, 1 (1): 1-10.
  • 25. Ronneberger, O., Fischer, P., Brox,T. (2015) U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical image computing and computer-assisted intervention, Munich, Germany, October 5-9, pp. 234-241. doi: 10.1007/978-3-319-24574-4_28
  • 26. Schaffer, S. (2003) Golden means: Assay instruments and the geography of precision in the Guinea trade, Instruments, Travel and Science, Routledge. p. 32-62.
  • 27. Singh, N. (2012) A rugged, precise and accurate new gravimetry method for the determination of gold: an alternative to fire assay method. SpringerPlus, 1 (1): 1-6. doi: 10.1186/2193-1801-1-14
  • 28. Smrcka, L., K. Misek, and J. Bednar (1965) The density of quenched gold. Cechoslovackij Fiziceskij Zurnal B, 15 (6): 418-424.
  • 29. Specification for Density hydrometers. British Standard 1991. BS718:1991.
Toplam 29 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yapay Zeka, Yazılım Mühendisliği, Bilgisayar Yazılımı
Bölüm Araştırma Makaleleri
Yazarlar

Yekta Said Can 0000-0002-6614-0183

Proje Numarası TEYDEB 9150222 Akıllı Altın Tanıma Teknolojisi
Yayımlanma Tarihi 30 Nisan 2022
Gönderilme Tarihi 5 Ocak 2022
Kabul Tarihi 27 Şubat 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 27 Sayı: 1

Kaynak Göster

APA Can, Y. S. (2022). CLASSIFICATION OF ORIGINAL AND COUNTERFEIT GOLD MATTERS BY APPLYING DEEP NEURAL NETWORKS AND SUPPORT VECTOR MACHINES. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, 27(1), 89-102. https://doi.org/10.17482/uumfd.1054013
AMA Can YS. CLASSIFICATION OF ORIGINAL AND COUNTERFEIT GOLD MATTERS BY APPLYING DEEP NEURAL NETWORKS AND SUPPORT VECTOR MACHINES. UUJFE. Nisan 2022;27(1):89-102. doi:10.17482/uumfd.1054013
Chicago Can, Yekta Said. “CLASSIFICATION OF ORIGINAL AND COUNTERFEIT GOLD MATTERS BY APPLYING DEEP NEURAL NETWORKS AND SUPPORT VECTOR MACHINES”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 27, sy. 1 (Nisan 2022): 89-102. https://doi.org/10.17482/uumfd.1054013.
EndNote Can YS (01 Nisan 2022) CLASSIFICATION OF ORIGINAL AND COUNTERFEIT GOLD MATTERS BY APPLYING DEEP NEURAL NETWORKS AND SUPPORT VECTOR MACHINES. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 27 1 89–102.
IEEE Y. S. Can, “CLASSIFICATION OF ORIGINAL AND COUNTERFEIT GOLD MATTERS BY APPLYING DEEP NEURAL NETWORKS AND SUPPORT VECTOR MACHINES”, UUJFE, c. 27, sy. 1, ss. 89–102, 2022, doi: 10.17482/uumfd.1054013.
ISNAD Can, Yekta Said. “CLASSIFICATION OF ORIGINAL AND COUNTERFEIT GOLD MATTERS BY APPLYING DEEP NEURAL NETWORKS AND SUPPORT VECTOR MACHINES”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 27/1 (Nisan 2022), 89-102. https://doi.org/10.17482/uumfd.1054013.
JAMA Can YS. CLASSIFICATION OF ORIGINAL AND COUNTERFEIT GOLD MATTERS BY APPLYING DEEP NEURAL NETWORKS AND SUPPORT VECTOR MACHINES. UUJFE. 2022;27:89–102.
MLA Can, Yekta Said. “CLASSIFICATION OF ORIGINAL AND COUNTERFEIT GOLD MATTERS BY APPLYING DEEP NEURAL NETWORKS AND SUPPORT VECTOR MACHINES”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, c. 27, sy. 1, 2022, ss. 89-102, doi:10.17482/uumfd.1054013.
Vancouver Can YS. CLASSIFICATION OF ORIGINAL AND COUNTERFEIT GOLD MATTERS BY APPLYING DEEP NEURAL NETWORKS AND SUPPORT VECTOR MACHINES. UUJFE. 2022;27(1):89-102.

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