Application In Distinguishing Artificial Intelligence-Makened Images And Original Images With Visual Feature Extraction Using Ensemble Learning Algorithm
DOI:
https://doi.org/10.62123/aqila.v3i1.174Keywords:
Artificial Intelligence, Visual Feature Extraction, Ensemble LearningAbstract
The development of generative Artificial Intelligence (AI) technology enables computer systems to produce highly realistic images that closely resemble real photographs. This condition creates challenges in distinguishing AI-generated images from real images visually. This research aims to develop an image classification system capable of distinguishing AI-generated images and real images using visual feature extraction and ensemble learning algorithms.The research method consists of several stages including image preprocessing by resizing images to 256 × 256 pixels, visual feature extraction including RGB color histogram, grayscale intensity distribution, texture features using Gray Level Co-occurrence Matrix (GLCM), and edge features using the Canny Edge Detection method. The extracted features are then used as input for several classification algorithms such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest. Furthermore, model combination is performed using an ensemble learning method with a hard voting technique.The experimental results show that the Random Forest model achieved an accuracy of 65.71%, while the ensemble learning method achieved an accuracy of 65.00% with an F1-score of 0.6918. The developed system is also implemented as a web-based application using the Streamlit framework, allowing users to upload images and obtain prediction results directly. The results indicate that the combination of visual feature extraction and ensemble learning can be used as an approach to help identify AI-generated images and real images.
References
[1] A. Aggarwal, M. Mittal, and G. Battineni, “Generative adversarial network: An overview of theory and applications,” International Journal of Information Management Data Insights, vol. 1, no. 1, p. 100004, 2021.
[2] S. A. Alajaji et al., “Generative adversarial networks in digital histopathology: current applications, limitations, ethical considerations, and future directions,” Modern Pathology, vol. 37, no. 1, p. 100369, 2024.
[3] C. Kurniawan, “Implementasi Artificial Intelligence Dalam Penyelesaian Masalah,” Sinteks: Jurnal Teknik, vol. 4, no. 1, 2015.
[4] Developers, “Scikit-Learn: Machine Learning in Python.” Accessed: Jun. 12, 2026. [Online]. Available: https://scikit-learn.org/stable/
[5] P. Borugadda and H. K. Kalluri, “A comprehensive analysis of artificial intelligence, machine learning, deep learning and computer vision in food science,” Journal of Future Foods, 2025.
[6] Raisa Shofia and W. Aulia, “Perancangan Perangkat Makan Bersama Bertolak dari Tradisi Bajamba Minangkabau Menggunakan Metode Ekstraksi Unsur Visual,” Keluwih: Jurnal Sains dan Teknologi, vol. 4, no. 1, pp. 21–33, May 2023, doi: 10.24123/saintek.v4i1.5588.
[7] K. D. T. M. Milanez and M. J. C. Pontes, “Classification of edible vegetable oil using digital image and pattern recognition techniques,” Microchemical Journal, vol. 113, pp. 10–16, 2014.
[8] F. F. Maulana and N. Rochmawati, “Klasifikasi citra buah menggunakan convolutional neural network,” Journal of Informatics and Computer Science (JINACS), vol. 1, no. 02, pp. 104–108, 2019.
[9] A. Alotaibi, “Ensemble Deep Learning Approaches in Health Care: A Review.,” Computers, Materials & Continua, vol. 82, no. 3, 2025.
[10] U. Indahyanti, N. L. Azizah, and H. Setiawan, “Pendekatan ensemble learning untuk meningkatkan akurasi prediksi kinerja akademik mahasiswa,” Jurnal Sains dan Informatika, vol. 8, no. 2, 2022.
[11] S. Tyagi et al., “An Enhanced Diabetic Retinopathy Blindness Detection Using Deep Learning and Image Preprocessing Techniques,” Array, p. 100716, 2026.
[12] A. M. Nababan, U. R. P. Nasution, T. D. Pandiangan, F. Nadi, R. Budiarto, and R. F. Rahmat, “Extreme learning machine approach on heart abnormalities identification in ECG images,” International Journal of Electronics and Telecommunications, vol. 70, 2024.
[13] S. Hallur and A. Gavade, “Image feature extraction techniques: A comprehensive review,” Franklin Open, p. 100366, 2025.
[14] R. Bala, N. Duhan, and K. K. Bhatia, “Exploring Image Processing Techniques for Skin Lesion Detection focusing on Segmentation and Classification: A Systematic Review,” Biomed. Signal Process. Control, vol. 116, p. 109510, 2026.
[15] OpenCV Org, “OpenCV Documentation.” Accessed: Jun. 12, 2026. [Online]. Available: https://opencv.org/
[16] Team Pandas, “Pandas documentation.” Accessed: Jun. 12, 2026. [Online]. Available: https://pandas.pydata.org/
[17] R. Sharma, R. Gupta, and A. Sinha, “From image processing to artificial intelligence-driven tools: A comprehensive survey on the evolution of feature extraction methods in paintings,” Eng. Appl. Artif. Intell., vol. 161, p. 111794, 2025.
[18] Coorporation, “Visual studio code documentation. Retrieved from visual studio code.” Accessed: Jun. 12, 2026. [Online]. Available: https://code.visualstudio.com/
[19] J. Ezenarro, Á. García-Pizarro, O. Busto, A. de Juan, and R. Boqué, “Analysing olive ripening with digital image RGB histograms,” Anal. Chim. Acta, vol. 1280, p. 341884, 2023.
[20] Python Org, “Python Documentation. Retrieved from Python.” Accessed: Jun. 12, 2026. [Online]. Available: https://www.python.org/
[21] A. I. Awodeyi, O. A. Ibok, I. Omokaro, J. U. Ekwemuka, and M. O. Ighofiomoni, “Effective preprocessing techniques for improved facial recognition under variable conditions,” Franklin Open, vol. 10, p. 100225, 2025.
[22] Y. Liu, X. Wang, Z. Zhang, and F. Deng, “Deep learning based data augmentation for large-scale mineral image recognition and classification,” Miner. Eng., vol. 204, p. 108411, 2023.
[23] M. Z. Li, G. Liu, Z. Mao, Q. S. Yang, and J. W. Gu, “Two-dimensional motion estimation using phase-based image processing with Riesz transform,” Mech. Syst. Signal Process., vol. 188, p. 110044, Apr. 2023, doi: 10.1016/j.ymssp.2022.110044.
[24] K. Veena, K. Meena, Y. Teekaraman, R. Kuppusamy, and A. Radhakrishnan, “C SVM classification and KNN techniques for cyber crime detection,” Wirel. Commun. Mob. Comput., vol. 2022, no. 1, p. 3640017, 2022.
[25] O. U. Lenz, H. Bollaert, and C. Cornelis, “A unified weighting framework for evaluating nearest neighbour classification,” Fuzzy Sets Syst., vol. 519, p. 109516, 2025.
[26] M. Osenberg et al., “Classification of FIB/SEM-tomography images for highly porous multiphase materials using random forest classifiers,” J. Power Sources, vol. 570, p. 233030, 2023.
[27] S. Viswanathan, N. V. Sridharan, J. Rakkiyannan, and S. Vaithiyanathan, “Brake fault diagnosis using a voting ensemble of machine learning classifiers,” Results in Engineering, vol. 23, p. 102857, 2024.
[28] J. Boulos, V. Eglin, B. Kerautret, E. Larue, and J.-M. Côme, “Soil image classification and segmentation: A survey from deep learning, multimodal data and hybrid models,” Geodata and AI, p. 100053, 2026.
[29] Y. Wu and J. Wan, “A survey of text classification based on pre-trained language model,” Neurocomputing, vol. 616, p. 128921, 2025.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Moh Hafiz Naufal, Al-Khowarizmi
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
