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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING
Fachril Akbar
Department of Informatics Engineering, Universitas Malikussaleh
Computer Science & IT

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Facial Deepfake Detection System Using YOLOv11 and Xception Architecture Fachril Akbar; Nurdin Nurdin; Kurniawati Kurniawati
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13449

Abstract

The rapid development of artificial intelligence has led to the emergence of deepfakes, which pose serious threats to information security and public trust in digital media. This study develops a facial deepfake detection system that integrates YOLOv11 for face detection and the Xception architecture for classifying real and manipulated faces. YOLOv11 successfully localized all facial regions in the tested dataset with high confidence scores. The Xception model achieved a testing accuracy of 90.10%, with a Recall of 97.11% for the Fake class and an AUC of 0.98. Visual explanation using Grad-CAM showed that the model focused on critical areas such as the forehead, temples, and face boundaries to detect manipulation artifacts. The system was implemented as a desktop application named "Snap Detector" and passed black-box testing. However, the average processing speed of 6.26 FPS on an NVIDIA T4 GPU indicates that further optimization is needed for real-time performance.
Co-Authors Kurniawati Kurniawati Nurdin Nurdin