ORIGINAL RESEARCH article
Front. Big Data
Sec. Machine Learning and Artificial Intelligence
Volume 8 - 2025 | doi: 10.3389/fdata.2025.1569147
Design and Development of an Efficient RLNet Prediction Model for DeepFake Video Detection
Provisionally accepted
- VIT University, Vellore, India
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The widespread emergence of deepfake videos presents substantial challenges to the security and authenticity of digital content, necessitating robust detection methods. Deepfake detection remains challenging due to the increasing sophistication of forgery techniques. While existing methods often focus on spatial features, they may overlook crucial temporal information distinguishing real from fake content and need to investigate several other Convolutional Neural Network architectures on video-based deep fake datasets. This study introduces an RLNet deep learning framework that utilizes ResNet and Long Short Term Memory (LSTM) networks for high-precision deepfake video detection. The key objective is exploiting spatial and temporal features to discern manipulated content accurately. The proposed approach starts with preprocessing a diverse dataset with authentic and deepfake videos. The ResNet component captures intricate spatial anomalies at the frame level, identifying subtle manipulations.Concurrently, the LSTM network analyzes temporal inconsistencies across video sequences, detecting dynamic irregularities that signify deepfake content. Experimental findings indicate the efficacy of the integrated ResNet and LSTM methodology, with a detection accuracy of 95. 2% and fewer false positives relative to established techniques such as EfficientNet and RNN. Moreover, the framework improves the efficacy of deepfake detection in compressed videos, demonstrating its resilience in practical deployment contexts. This research contributes significantly to digital media forensics by providing an advanced tool for detecting deepfake videos, enhancing the security and integrity of digital content. The efficacy and resilience of the proposed system are evidenced by deepfake detection, while our visualization-based interpretability provides insights into our model.
Keywords: Resnet, Long Short Term Memory Networks (LSTM), deep learning, DeepFake Detection, Explainable artificial intelligence
Received: 31 Jan 2025; Accepted: 17 Jun 2025.
Copyright: © 2025 Bhandarkawthekar, T M, Sharma and K. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Navamani T M, VIT University, Vellore, India
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