Enhancing accuracy in forgery signature detection: Deep learning approaches with support vector machines
To detect forgeries in signature images using a state-of-the-art deep learning Support Vector Machine (SVM) algorithm based on parameters extracted from the data set. 44 samples total are used in the study, which is split into two groups of 22. Group 1 utilizes CNN-xg, whereas Group 2 employs SVM. C...
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| creator | Jayaprakash, P. Ramkumar, G. Christy, S. Poovizhi, T. Selvaperumal, S. K. Lakshamanan, R. Gladith, N. A. |
| description | To detect forgeries in signature images using a state-of-the-art deep learning Support Vector Machine (SVM) algorithm based on parameters extracted from the data set. 44 samples total are used in the study, which is split into two groups of 22. Group 1 utilizes CNN-xg, whereas Group 2 employs SVM. Colab software specialized for machine learning is used to run the code. According to simulation findings, the CNN-xg Algorithm obtains a greater reliability of 96.82%, while the SVM achieves reliability of 84.80%; both algorithms have the same significance values of 0.0004 (p < 0.05). CNN-xg identifies forged signatures in the provided dataset more correctly than SVM, demonstrating superior performance. |
| doi_str_mv | 10.1063/5.0229469 |
| format | Conference Proceeding |
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CNN-xg identifies forged signatures in the provided dataset more correctly than SVM, demonstrating superior performance.</description><subject>Algorithms</subject><subject>Artificial neural networks</subject><subject>Deep learning</subject><subject>Image enhancement</subject><subject>Machine learning</subject><subject>Software reliability</subject><subject>Support vector machines</subject><issn>0094-243X</issn><issn>1551-7616</issn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2024</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNotkM1OwzAQhC0EEqVw4A0scUNKsbOxE3NDpfxIlbj0wC1y3E3jqnWC7YD69k1oT3vYb2d2hpB7zmacSXgSM5amKpPqgky4EDzJJZeXZMKYypI0g-9rchPClrFU5XkxIc3CNdoZ6zZUG9N7bQ7UOlq3foP-QIPdOB17j3SNEU20rXumr4gd3aH27v-s63yrTYOB_tnY0NB3Xesj_R3w1tP9sLIOwy25qvUu4N15TsnqbbGafyTLr_fP-csy6SSoBJQEY7CqVKUNqzKRQcV0roqcFzlw1IURCFjrVNZmrUEUgtWIADmirBjAlDycZIenfnoMsdy2vXeDYwlMFcWoNFKPJyoYG_WYquy83Wt_KDkrxyJLUZ6LhCPC82dl</recordid><startdate>20240830</startdate><enddate>20240830</enddate><creator>Jayaprakash, P.</creator><creator>Ramkumar, G.</creator><creator>Christy, S.</creator><creator>Poovizhi, T.</creator><creator>Selvaperumal, S. 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A.</au><au>Cheong, Alexander Chee Hon</au><au>Perumal, Sathish Kumar Selva</au><au>Yong, Lau Chee</au><au>Sivanesan, Siva Kumar</au><au>Thiruchelvam, Vinesh</au><au>Nataraj, Chandrasekharan</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Enhancing accuracy in forgery signature detection: Deep learning approaches with support vector machines</atitle><btitle>AIP conference proceedings</btitle><date>2024-08-30</date><risdate>2024</risdate><volume>3161</volume><issue>1</issue><issn>0094-243X</issn><eissn>1551-7616</eissn><coden>APCPCS</coden><abstract>To detect forgeries in signature images using a state-of-the-art deep learning Support Vector Machine (SVM) algorithm based on parameters extracted from the data set. 44 samples total are used in the study, which is split into two groups of 22. Group 1 utilizes CNN-xg, whereas Group 2 employs SVM. Colab software specialized for machine learning is used to run the code. According to simulation findings, the CNN-xg Algorithm obtains a greater reliability of 96.82%, while the SVM achieves reliability of 84.80%; both algorithms have the same significance values of 0.0004 (p < 0.05). CNN-xg identifies forged signatures in the provided dataset more correctly than SVM, demonstrating superior performance.</abstract><cop>Melville</cop><pub>American Institute of Physics</pub><doi>10.1063/5.0229469</doi><tpages>6</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0094-243X |
| ispartof | AIP conference proceedings, 2024, Vol.3161 (1) |
| issn | 0094-243X 1551-7616 |
| language | eng |
| recordid | cdi_scitation_primary_10_1063_5_0229469 |
| source | AIP Journals Complete |
| subjects | Algorithms Artificial neural networks Deep learning Image enhancement Machine learning Software reliability Support vector machines |
| title | Enhancing accuracy in forgery signature detection: Deep learning approaches with support vector machines |
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