Unmasking Facial DeepFakes: A Robust Multiview Detection Framework for Natural Images

• URL: http://arxiv.org/abs/2510.15576v1
• Date: Fri, 17 Oct 2025 12:16:04 GMT
• Title: Unmasking Facial DeepFakes: A Robust Multiview Detection Framework for Natural Images
• Authors: Sami Belguesmia, Mohand Saïd Allili, Assia Hamadene,
• Abstract summary: We propose a multi-view architecture that enhances DeepFake detection by analyzing facial features at multiple levels.<n>Our approach integrates three specialized encoders, a global view encoder for detecting boundary inconsistencies, a middle view encoder for analyzing texture and color alignment, and a local view encoder for capturing distortions in expressive facial regions.<n>By fusing features from these encoders, our model achieves superior performance in detecting manipulated images, even under challenging pose and lighting conditions.
• Score: 1.5049442691806052
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: DeepFake technology has advanced significantly in recent years, enabling the creation of highly realistic synthetic face images. Existing DeepFake detection methods often struggle with pose variations, occlusions, and artifacts that are difficult to detect in real-world conditions. To address these challenges, we propose a multi-view architecture that enhances DeepFake detection by analyzing facial features at multiple levels. Our approach integrates three specialized encoders, a global view encoder for detecting boundary inconsistencies, a middle view encoder for analyzing texture and color alignment, and a local view encoder for capturing distortions in expressive facial regions such as the eyes, nose, and mouth, where DeepFake artifacts frequently occur. Additionally, we incorporate a face orientation encoder, trained to classify face poses, ensuring robust detection across various viewing angles. By fusing features from these encoders, our model achieves superior performance in detecting manipulated images, even under challenging pose and lighting conditions.Experimental results on challenging datasets demonstrate the effectiveness of our method, outperforming conventional single-view approaches

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