CNN-Based Model for Copy Detection Pattern Estimation and Authentication
Counterfeiting has been one of the crimes of the 21st century. One of the methods to overcome product counterfeiting is a copy detection pattern (CDP) stamped on the product. CDP is a copy-sensitive pattern that leads to quality degradation of the pattern after the print and scan process. The amount...
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| Format: | Article |
| Language: | English |
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Universitas Gadjah Mada
2023-02-01
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| Series: | Jurnal Nasional Teknik Elektro dan Teknologi Informasi |
| Subjects: | |
| Online Access: | https://jurnal.ugm.ac.id/v3/JNTETI/article/view/6205 |
| _version_ | 1811158988176228352 |
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| author | Syukron Abu Ishaq Alfarozi Azkario Rizky Pratama |
| author_facet | Syukron Abu Ishaq Alfarozi Azkario Rizky Pratama |
| author_sort | Syukron Abu Ishaq Alfarozi |
| collection | DOAJ |
| description | Counterfeiting has been one of the crimes of the 21st century. One of the methods to overcome product counterfeiting is a copy detection pattern (CDP) stamped on the product. CDP is a copy-sensitive pattern that leads to quality degradation of the pattern after the print and scan process. The amount of information loss is used to distinguish between original and fake CDPs. This paper proposed a CDP estimation model based on the convolutional neural network (CNN), namely, CDP-CNN. The CDP-CNN addresses the spatial dependency of the image patch. Thus, it should be better than the state-of-the-art model that uses a multi-layer perceptron (MLP) architecture. The proposed model had an estimation bit error rate (BER) of 9.91% on the batch estimation method. The error rate was 9% lower than the previous method that used an autoencoder MLP model. The proposed model also had a lower number of parameters compared to the previous method. The effect of preprocessing, namely the use of an unsharp mask, was tested using a statistical testing method. The effect of preprocessing had no significant difference except in the batch estimation scheme where the unsharp mask filter reduced the error rate by at least 0.5%. In addition, the proposed model was also used for the authentication method. The authentication using the estimation model had a good separation distribution to distinguish the fake and original CDPs. Thus, the CDP can still be used as the authentication method with reliable performance. It helps anti-counterfeiting on product distribution and reduces negative impacts on various sectors of the economy. |
| first_indexed | 2024-04-10T05:33:31Z |
| format | Article |
| id | doaj.art-810484cb29544cde8fb01f30ef273cc1 |
| institution | Directory Open Access Journal |
| issn | 2301-4156 2460-5719 |
| language | English |
| last_indexed | 2024-04-10T05:33:31Z |
| publishDate | 2023-02-01 |
| publisher | Universitas Gadjah Mada |
| record_format | Article |
| series | Jurnal Nasional Teknik Elektro dan Teknologi Informasi |
| spelling | doaj.art-810484cb29544cde8fb01f30ef273cc12023-03-07T02:41:08ZengUniversitas Gadjah MadaJurnal Nasional Teknik Elektro dan Teknologi Informasi2301-41562460-57192023-02-01121444910.22146/jnteti.v12i1.62056205CNN-Based Model for Copy Detection Pattern Estimation and AuthenticationSyukron Abu Ishaq Alfarozi0Azkario Rizky Pratama1Universitas Gadjah MadaUniversitas Gadjah MadaCounterfeiting has been one of the crimes of the 21st century. One of the methods to overcome product counterfeiting is a copy detection pattern (CDP) stamped on the product. CDP is a copy-sensitive pattern that leads to quality degradation of the pattern after the print and scan process. The amount of information loss is used to distinguish between original and fake CDPs. This paper proposed a CDP estimation model based on the convolutional neural network (CNN), namely, CDP-CNN. The CDP-CNN addresses the spatial dependency of the image patch. Thus, it should be better than the state-of-the-art model that uses a multi-layer perceptron (MLP) architecture. The proposed model had an estimation bit error rate (BER) of 9.91% on the batch estimation method. The error rate was 9% lower than the previous method that used an autoencoder MLP model. The proposed model also had a lower number of parameters compared to the previous method. The effect of preprocessing, namely the use of an unsharp mask, was tested using a statistical testing method. The effect of preprocessing had no significant difference except in the batch estimation scheme where the unsharp mask filter reduced the error rate by at least 0.5%. In addition, the proposed model was also used for the authentication method. The authentication using the estimation model had a good separation distribution to distinguish the fake and original CDPs. Thus, the CDP can still be used as the authentication method with reliable performance. It helps anti-counterfeiting on product distribution and reduces negative impacts on various sectors of the economy.https://jurnal.ugm.ac.id/v3/JNTETI/article/view/6205copy detection patternconvolutional neural networkanti-counterfeiting |
| spellingShingle | Syukron Abu Ishaq Alfarozi Azkario Rizky Pratama CNN-Based Model for Copy Detection Pattern Estimation and Authentication Jurnal Nasional Teknik Elektro dan Teknologi Informasi copy detection pattern convolutional neural network anti-counterfeiting |
| title | CNN-Based Model for Copy Detection Pattern Estimation and Authentication |
| title_full | CNN-Based Model for Copy Detection Pattern Estimation and Authentication |
| title_fullStr | CNN-Based Model for Copy Detection Pattern Estimation and Authentication |
| title_full_unstemmed | CNN-Based Model for Copy Detection Pattern Estimation and Authentication |
| title_short | CNN-Based Model for Copy Detection Pattern Estimation and Authentication |
| title_sort | cnn based model for copy detection pattern estimation and authentication |
| topic | copy detection pattern convolutional neural network anti-counterfeiting |
| url | https://jurnal.ugm.ac.id/v3/JNTETI/article/view/6205 |
| work_keys_str_mv | AT syukronabuishaqalfarozi cnnbasedmodelforcopydetectionpatternestimationandauthentication AT azkariorizkypratama cnnbasedmodelforcopydetectionpatternestimationandauthentication |