International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Volume 7 Issue 3
May-June 2025

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Face-Aware Deepfake Detection Using ResNeXt-101 and Real-Time Feedback Integration

Author(s) Ms. Tarandeep Kaur, Mr. Akankshar Prashar, Dr. Pankaj Deep Kaur
Country India
Abstract The growing sophistication of deepfake technology has introduced serious concerns around the authenticity of digital video content. As manipulated videos become increasingly indistinguishable from real ones, the urgency for reliable and efficient detection systems has never been greater. This paper presents a face-aware deepfake video detection framework leveraging the ResNeXt-101 deep convolutional architecture, enhanced by a real-time feedback interface for continuous model improvement. The proposed approach focuses on extracting and analyzing facial features from sampled video frames, applying a carefully designed preprocessing pipeline to standardize inputs while preserving crucial visual cues. By fine-tuning a pre-trained ResNeXt-101 network using transfer learning on the FaceForensics++ dataset, the system achieved an overall accuracy of 89.47%, with particularly strong recall for detecting fake content (93%) and high precision for real videos (91%). A user-friendly web interface built with Gradio allows users to upload videos, receive immediate classification results, and flag incorrect predictions, creating a loop for iterative model enhancement. This paper also explores the system’s robustness across varying conditions and evaluates alternative architectures. The results underscore the practical viability of deep learning-based solutions in combatting deepfakes and highlight the importance of accessible, adaptive tools in maintaining trust in digital media.
Keywords Deepfake Detection, Face-Aware Preprocessing, Transfer Learning, Temporal Analysis, CNN
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 7, Issue 3, May-June 2025
Published On 2025-06-25
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E-ISSN 2582-2160
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