Classification and Analysis of Real and Fake Aerial Vehicle Images Using Machine Learning

Hasan Aksoy; Ziya Ozcelik; Yavuz Taspinar

doi:10.47134/jtsi.v3i1.5345

Authors

Hasan Aksoy Selcuk University https://orcid.org/0009-0005-2516-5658
Ziya Ozcelik Selcuk University https://orcid.org/0000-0002-6567-2671
Yavuz Selim Taspinar Selcuk University https://orcid.org/0000-0002-7278-4241

DOI:

https://doi.org/10.47134/jtsi.v3i1.5345

Keywords:

Aerial Vehicle Imagery, Image Classification, Machine Learning, Artificial Neural Networks, False Image Detection

Abstract

Aircraft are widely used in both military and civilian fields today. Detecting aircraft in the airspace is of great strategic and societal importance. In recent years, distinguishing images generated by artificial intelligence from real images has become increasingly difficult. This article presents a study on the classification of real aircraft images and AI-generated aircraft images by machine learning algorithms. Six classifications were obtained from 300 images in the dataset. These classifications are: fake commercial aircraft AI, fake military aircraft AI, fake private aircraft AI, real commercial aircraft, real military aircraft, and real private aircraft. These data were classified using common machine learning models such as Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Logistic Regression (LR). Accuracy, Precision, Recall, and F1 Score metrics were used to analyze the classification success of these models. ROC was used for a detailed analysis of the classification success of the models. According to the results obtained, the ANN model achieved a classification success rate of 96.6%, the KNN model 90.4%, the SVM model 96.7%, and the LR model 96.5%. The highest classification success rate was obtained from the SVM model. These results show that all models achieved similar classification success rates, with the KNN model achieving a lower classification success rate than the others. In conclusion, it can be said that all models can be used in the classification of aircraft images.

References

AlDosari, K., Osman, A., Elharrouss, O., Al-Maadeed, S., & Chaari, M. Z. (2024). Drone-type-Set: Drone types detection benchmark for drone detection and tracking. 2024 International Conference on Intelligent Systems and Computer Vision (ISCV).

Aydın, İ., & Kızılay, E. (2022). Development of a new light-weight convolutional neural network for acoustic-based amateur drone detection. Applied Acoustics, 193, 108773.

Cinar, I., & Taspinar, Y. S. (2024). Detection of machine failures with machine learning methods. Proceedings of International Conference on Intelligent Systems and New Applications,

Cinarer, G., Taspinar, Y. S., & Zeybek, M. (2025). Detection of Aerial Vehicles Using Satellite Imagery: Comparative Analysis of U‐Net Segmentation Model and YOLO Object Detection Model. International Journal of Aerospace Engineering, 2025(1), 5599522.

Classification Model Dataset. https://www.kaggle.com/datasets/samanthaclark55/fake-aircraft-images.

DERTLI, B., & KOKLU, M. (2025). Classification Of Hair Types with Deep Learning Methods.

Ghazlane, Y., Gmira, M., & Medromi, H. (2024). Development of a vision-based anti-drone identification friend or foe model to recognize birds and drones using deep learning. Applied Artificial Intelligence, 38(1), 2318672.

İnan, T. T. (2023). Aircraft damage classification by using machine learning methods. International Journal of Aviation, Aeronautics, and Aerospace, 10(2), 4.

Isik, M., Ozulku, B., Kursun, R., Taspinar, Y. S., Cinar, I., Yasin, E. T., & Koklu, M. (2024). Automated classification of hand-woven and machine-woven carpets based on morphological features using machine learning algorithms. The Journal of The Textile Institute, 115(12), 2650-2659.

Kassab, M., Zitar, R. A., Barbaresco, F., & Seghrouchni, A. E. F. (2024). Drone detection with improved precision in traditional machine learning and less complexity in single-shot detectors. IEEE Transactions on Aerospace and Electronic Systems, 60(4), 3847-3859.

Kilci, O., & Koklu, M. (2025). Machine learning-based detection of solar panel surface defects using deep features from InceptionV3. 4th International Conference on Trends in Advanced Research Konya, Türkiye.

Koklu, M., Cinar, I., & Taspinar, Y. S. (2021). Classification of rice varieties with deep learning methods. Computers and electronics in agriculture, 187, 106285.

Ozkan, I. A., & Koklu, M. (2017). Skin lesion classification using machine learning algorithms. International Journal of Intelligent Systems and Applications in Engineering, 5(4), 285-289.

Sayed, A. N., Ramahi, O. M., & Shaker, G. (2023). Machine learning for UAV classification employing mechanical control information. IEEE Transactions on Aerospace and Electronic Systems, 60(1), 68-81.

Shafiq, K. (2023). Drone Detection and Classification using Machine Learning Université d'Ottawa/University of Ottawa].

Taheri Gorji, H., Wilson, N., VanBree, J., Hoffmann, B., Petros, T., & Tavakolian, K. (2023). Using machine learning methods and EEG to discriminate aircraft pilot cognitive workload during flight. Scientific Reports, 13(1), 2507.

Taspinar, Y. S., & Cinar, I. (2024). Distinguishing between AI images and real images with hybrid image classification methods. 2024 13th Mediterranean Conference on Embedded Computing (MECO).

Uddin, Z., Qamar, A., Alharbi, A. G., Orakzai, F. A., & Ahmad, A. (2022). Detection of multiple drones in a time-varying scenario using acoustic signals. Sustainability, 14(7), 4041.

Wang, Y., Wang, T., Zhou, X., Cai, W., Liu, R., Huang, M., Jing, T., Lin, M., He, H., & Wang, W. (2022). TransEffiDet: aircraft detection and classification in aerial images based on EfficientDet and transformer. Computational Intelligence and Neuroscience, 2022(1), 2262549.

Yasin, E. T., Ropelewska, E., Kursun, R., Cinar, I., Taspinar, Y. S., Yasar, A., Mirjalili, S., & Koklu, M. (2025). Optimized feature selection using gray wolf and particle swarm algorithms for corn seed image classification. Journal of Food Composition and Analysis, 145, 107738.

Yoshihashi, R., Trinh, T. T., Kawakami, R., You, S., Iida, M., & Naemura, T. (2017). Differentiating objects by motion: Joint detection and tracking of small flying objects. arXiv preprint arXiv:1709.04666.

Zheng, Y., Chen, Z., Lv, D., Li, Z., Lan, Z., & Zhao, S. (2021). Air-to-air visual detection of micro-uavs: An experimental evaluation of deep learning. IEEE Robotics and automation letters, 6(2), 1020-1027.

Zohra, S. F., Nawal, C., & El Houda, D. N. (2023). Optical Detection of UAVs Using Deep Convolutional Neural Networks. 2023 2nd International Conference on Electronics, Energy and Measurement (IC2EM).