Year: 2025
Type: article
Abstract: The emergence of deepfake technology, which relies on generative adversarial networks (GANs), has raised substantial concerns in the realm of digital media. This technology enables the manipulation of facial features in videos, leading to potential misuse for spreading false information, misrepresentation, and identity theft. As a result, there is a pressing need to establish robust methods for detecting deepfakes effectively. Detecting deepfake videos is particularly difficult due to their increasingly realistic appearance and the sophisticated techniques involved in their creation. This research introduces an innovative approach to deepfake detection that leverages advanced deep learning methodologies. Specifically, the study employs Convolutional Neural Networks (CNNs) in combination with Recurrent Neural Networks (RNNs), with a particular focus on Long Short-Term Memory (LSTM) networks, to enhance the identification process for deepfake content. The proposed model is trained on comprehensive datasets, including FaceForensics++ and the Deepfake Detection Challenge (DFDC). To bolster detection accuracy, the methodology includes a pre-processing pipeline that not only reduces the frame rates of video inputs but also isolates and focuses on facial regions using Haar Cascade classifiers. This dual approach of analyzing both spatial and temporal inconsistencies within video frames contributes significantly to the overall effectiveness of deepfake detection. Through rigorous testing, the proposed method has demonstrated a high level of accuracy in distinguishing between authentic and manipulated videos, showcasing its potential as a reliable solution in the ongoing fight against digital media fraud. It is crucial for researchers and practitioners in the field of video forensics and digital media security to further explore and refine such advanced detection techniques
Author: Prof. Amol Gosavi
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4409924710