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Fatoni, Fatoni
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Journal : Journal of Applied Data Sciences

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Fake vs Real Image Detection Using Deep Learning Algorithm Fatoni, Fatoni; Kurniawan, Tri Basuki; Dewi, Deshinta Arrova; Zakaria, Mohd Zaki; Muhayeddin, Abdul Muniif Mohd
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.490

Abstract

The purpose of this research project is to address the growing issues presented by modified visual information by developing a deep learning model for identifying between real and fake images. To enhance accuracy, this project evaluates the effectiveness of deep learning algorithms such as Residual Neural Network (ResNet), Visual Geometry Group 16 (VGG16), and Convolutional Neural Network (CNN) together with Error Level Analysis (ELA) as preprocessing the dataset. The CASIA dataset contains 7,492 real images and 5,124 fake images. The images included are from a wide range of random subjects, including buildings, fruits, animals, and more, providing a comprehensive dataset for model training and validation. This research examined models' effectiveness through experiments, measuring their training and validation accuracies. It comes out with the best accuracy of each model, which is for Convolutional Neural Network (CNN), 94% for training accuracy, and validation accuracy of 92%. For VGG16, with both training and validation accuracy reaching 94%. Lastly, Residual Neural Network (ResNet) demonstrated optimal performance with 95% training accuracy and 93% validation accuracy. This project also constructs a system prototype for practical applications, offering an interface for real-world testing. When integrating into the system prototype, only Residual Neural Network (ResNet) shows consistency and effectiveness when predicting both fake and real images, and this led to the decision to choose ResNet for integration into the system. Furthermore, the project identified several areas for improvement. Firstly, expanding the model comparison for discovering more successful algorithms. Next, improving the dataset preprocessing phase by incorporating filtering or denoising techniques. Lastly, refining the system prototype for greater appeal and user-friendliness has the potential to attract a larger audience.
Co-Authors Aan Restu Mukti, Aan Restu Abdul Rokhim Afriyudi Ahmad Syazili Ahmad Zakaria Ainur Rosidah Akbar, Putra Wahyudi Al Kautsar, Muhammad Kevin Aldiansyah, Aldiansyah Alim, Bahrul alimin alimin Alimsyah, Andi Saiful Andri Andri Antoni, Darius Awaluddin Awaluddin Bintang, Muhammad Yuan Chandra, Winoto Darwin Darwin dedi irawan Deni Erlansyah Deni Suwardiman Dewi, Deshinta Arrova Ernawati, Eka Faisal, M Imam Fitriani, Endah Hartono, Susilo Hendro Saksono, Prihambodo Heri Kuswoyo, Agus Herliawan, Aditya Ihsan, Andi Ilham, Firgi Anto Irwansyah Irwansyah IS, Nina Paramytha Ishak Bachtiar Jemakmun, Jemakmun Juhanis, Juhanis Julaihah, Siti Julhadi Kamadi, La Karim, Achmad Kelvin, M. Kurniawan, Tri Basuki Kusnindar, Arum Arupi M. Nurhadi Maharani, Nabila Maria Ulfa Meria, Desi Mestiria Harbani Sitepu Miranti, Amelia Tya MMSI Irfan ,S. Kom Mohamad Firdaus Muhamad Nasir, Muhamad Muhammad Fadli Muhammad Nasir Muhayeddin, Abdul Muniif Mohd Murwani, Simping Setyo Nurfida, Anita Paramitha IS, Nina Permata, Sindi Pratama, Heri Okta Prawira, Wanda Yudha Purwanto, Timur Dali Qowiyah Al Zahro, Yayang Qowiyah Al Zahro Rahmi Rahmi Raswadi, Mohammad Dika Ridwan, Andi Robby Hidayat Rosyad, Farlin Sabeli Aliya Sonianto Sonianto St. Zulaiha Nurhajarurahmah Suryayusra, Suryayusra Susanto, Is Susi Irianti Sustiyono, Agus Suyanto Suyanto Syafir, Muhammad Isnawan Syaputra, Hadi Tamsir Ariyadi Triana, Clarisa Ully Wulandari, Ully Waluyo Erry Wahyudi Widyanto Widyanto Wijaya, Alek Wiwin Agustian Zakaria, Mohd Zaki Zuhriyadi, Ilman