Aug 2026· JOURNAL OF APPLIED INFORMATICS AND COMPUTING· Vol 10, pp. 3338-3346· 0 citations· 20 references
TL;DR
This study develops a facial deepfake detection system that integrates YOLOv11 for face detection and the Xception architecture for classifying real and manipulated faces and passed black-box testing.
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.
A Deepfake detection framework based on MobileNetV2, which uses effective convolutional feature extraction to accurately classify real and fake facial photos to guarantee consistent input quality and improve the discriminative features for detection is suggested.
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A novel method for video deepfake detection that assimilates the Pelican Optimization algorithm with a DL model jointly named as Pelican Attention Stacked Bidirectional Long-Short Term Memory (PAttSBiL), aimed at improving recognition accuracy and efficacy is presented.
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The rapid growth of deepfake videos has become a significant threat to the believability of visual media, enabling misinformation, manipulation, and damage on a large scale. Even though recent detectors using deep learning methods exhibit high accuracy in identifying deepfake videos, these detectors are often black box...
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The demand for efficient and accurate face detection in real-time applications has increased significantly with the rise of Internet of Things (IoT) and embedded vision systems. Deploying deep learning models on low-power edge devices such as the Raspberry Pi 4 presents challenges related to limited memory, computation...
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Face recognition is a vital area of research in computer vision due to its significant roles in biometric identification, security systems, healthcare automation, and crime prevention. This study aims to make a comprehensive comparison between deep learning models (ResNet 50 & InceptionV3) for face recognition through...
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A novel multimodal approach to deepfake detection, integrating spatial, temporal, and audio features, which is highly efficient in addressing deepfake challenges in the digital forensic field and tested on various benchmarking datasets, yielding higher accuracy than current methods.
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