2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 18 , Issue 2 , PP: 233-250, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Ahmed Alhussen 1
Doi: https://doi.org/10.54216/FPA.180217
With the latest developments in computer vision, processing, accurate deepfakes (DF) require powerful tools. Recent research has developed a useful technique for identifying DFs in networks. The inter-frame differences of the gathered media streams, however, are beyond the scope of most methods. In this research, an Optimized Gaussian Convolutional Neural Network Framework for Enhanced Detection of Deepfakes in Digital Media (OGCNN-DDF-DM) is proposed. Initially the input images are gathered using the Face Forensics++ (FF++), and Deep Fake Detection Challenge dataset (DFDC) datasets. Then the Multi-Window Savitzky-Golay Filter (MWSGF) is used to improve quality of the DF images and reduce noise. Afterwards, Simple Contrastive Graph Clustering (SCGC) achieves segmentation. Here, the image's facial regions are segmented. Then, the texture features are extracted using Revised Tunable Q-Factor Wavelet Transform (RTQWT) is introduced. The extracted features are fed to Gaussian Convolutional Neural Network (GCNN) to categorize the image as real or fake. Finally, Gooseneck Barnacle Optimization Algorithm (GBOA) is proposed to improve the GCNN classifier. Performance parameters including accuracy, precision, recall, specificity, ROC, and computation time are examined. The introduced method attained an accuracy of 99.6% and the precision of 98.9% on the FaceForensics++ dataset, and 99.5% and 98.6% on the DFDC dataset, respectively.
DeepFake Detection , Multi-Window Savitzky-Golay Filter , Simple Contrastive Graph Clustering , RTQWT , Hybrid Optimization , Gooseneck Barnacle Optimization Algorithm , Optimized Gaussian Convolutional Neural Network
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