ArticleSensors (Basel, Switzerland)2021
A New Deep Learning-Based Methodology for Video Deepfake Detection Using XGBoost.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Attention-augmented hybrid framework with evolutionary optimization for robust deepfake detection.Scientific reports · 2026Article
- Unmasking digital deceptions: An integrative review of deepfake detection, multimedia forensics, and cybersecurity challenges.MethodsX · 2025Review
- Moving objects detection based on tensor ring low rank decomposition.Scientific reports · 2025Article
- Exposing Face Manipulation Based on Generative Adversarial Network-Transformer and Fake Frequency Noise Traces.Sensors (Basel, Switzerland) · 2025Article
- Possible Health Benefits and Risks of DeepFake Videos: A Qualitative Study in Nursing Students.Nursing reports (Pavia, Italy) · 2024Article
- Interpol review of forensic video analysis, 2019-2022.Forensic science international. Synergy · 2023Review
- A Review of Image Processing Techniques for Deepfakes.Sensors (Basel, Switzerland) · 2022Review
- A Real-Time Zanthoxylum Target Detection Method for an Intelligent Picking Robot under a Complex Background, Based on an Improved YOLOv5s Architecture.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
4 authors.
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Abstract
Currently, face-swapping deepfake techniques are widely spread, generating a significant number of highly realistic fake videos that threaten the privacy of people and countries. Due to their devastating impacts on the world, distinguishing between real and deepfake videos has become a fundamental issue. This paper presents a new deepfake detection method: you only look once-convolutional neural network-extreme gradient boosting (YOLO-CNN-XGBoost). The YOLO face detector is employed to extract the face area from video frames, while the InceptionResNetV2 CNN is utilized to extract features from these faces. These features are fed into the XGBoost that works as a recognizer on the top level of the CNN network. The proposed method achieves 90.62% of an area under the receiver operating characteristic curve (AUC), 90.73% accuracy, 93.53% specificity, 85.39% sensitivity, 85.39% recall, 87.36% precision, and 86.36% F1-measure on the CelebDF-FaceForencics++ (c23) merged dataset. The experimental study confirms the superiority of the presented method as compared to the state-of-the-art methods.
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