Volume 18 • Issue 2 • PP: 233-250 • 2025
Optimized Gaussian Convolutional Neural Network Framework for Enhanced Detection of Deepfakes in Digital Media
Open Access & Copyright
© 2025 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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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.
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References
[1] S. Sadiq, T. Aljrees, and S. Ullah, “Deepfake detection on social media: Leveraging deep learning and FastText embeddings for identifying machine-generated tweets,” IEEE Access, vol. 11, 2023.
[2] A. Heidari, N. J. Navimipour, H. Dag, S. Talebi, and M. Unal, “A novel blockchain-based deepfake detection method using federated and deep learning models,” Cognitive Computation, pp. 1–19, 2024.
[3] S. Albahli and M. Nawaz, “MedNet: Medical deepfakes detection using an improved deep learning approach,” Multimedia Tools and Applications, vol. 83, no. 16, pp. 48357–48375, 2024.
[4] S. Suratkar and F. Kazi, “Deep fake video detection using transfer learning approach,” Arabian Journal for Science and Engineering, vol. 48, no. 8, pp. 9727–9737, 2023.
[5] R. U. Maheshwari et al., “Advanced plasmonic resonance-enhanced biosensor for comprehensive real-time detection and analysis of deepfake content,” Plasmonics, pp. 1–18, 2024.
[6] Y. Salini and J. HariKiran, “Deepfake videos detection using crowd computing,” International Journal of Information Technology, vol. 16, no. 7, pp. 4547–4564, 2024.
[7] L. Cunha, L. Zhang, B. Sowan, C. P. Lim, and Y. Kong, “Video deepfake detection using particle swarm optimization improved deep neural networks,” Neural Computing and Applications, vol. 36, no. 15, pp. 8417–8453, 2024.
[8] M. Karaköse, H. Yetış, and M. Çeçen, “A new approach for effective medical deepfake detection in medical images,” IEEE Access, vol. 11, 2024.
[9] J. Gao et al., “Texture and artifact decomposition for improving generalization in deep-learning-based deepfake detection,” Engineering Applications of Artificial Intelligence, vol. 133, p. 108450, 2024.
[10] A. Hashmi, S. A. Shahzad, C. W. Lin, Y. Tsao, and H. M. Wang, “AVTENet: Audio-visual transformer-based ensemble network exploiting multiple experts for video deepfake detection,” IEEE Transactions on Multimedia, vol. 25, pp. 1234–1245, 2023.
[11] A. A. Khan et al., “Digital forensics for the socio-cyber world (DF-SCW): A novel framework for deepfake multimedia investigation on social media platforms,” Egyptian Informatics Journal, vol. 27, p. 100502, 2024.
[12] S. Vashishtha et al., “Optifake: Optical flow extraction for deepfake detection using ensemble learning technique,” Multimedia Tools and Applications, pp. 1–19, 2024.
[13] B. Yan, C. T. Li, and X. Lu, “JRC: Deepfake detection via joint reconstruction and classification,” Neurocomputing, p. 127862, 2024.
[14] S. K. Panda, T. Diwan, O. G. Kakde, and J. V. Tembhurne, “Improvised detection of deepfakes from visual inputs using lightweight deep ensemble model,” Multimedia Tools and Applications, vol. 82, no. 13, pp. 20101–20118, 2023.
[15] S. A. Khan and D. T. Dang-Nguyen, “Deepfake detection: Analysing model generalisation across architectures, datasets and pre-training paradigms,” IEEE Access, vol. 11, 2023.
[16] S. R. Ahmed and E. Sonuç, “Evaluating the effectiveness of rationale-augmented convolutional neural networks for deepfake detection,” Soft Computing, pp. 1–12, 2023.
[17] R. U. Maheshwari and B. Paulchamy, “Securing online integrity: A hybrid approach to deepfake detection and removal using Explainable AI and Adversarial Robustness Training,” Automatika, vol. 65, no. 4, pp. 1517–1532, 2024.
[18] N. Kumar and A. Kundu, “Cybersecurity-focused deepfake detection system using big data,” SN Computer Science, vol. 5, no. 6, p. 752, 2024.
[19] S. Kingra, N. Aggarwal, and N. Kaur, “SiamNet: Exploiting source camera noise discrepancies using Siamese network for deepfake detection,” Information Sciences, vol. 645, p. 119341, 2023.
[20] S. Mathews, S. Trivedi, A. House, S. Povolny, and C. Fralick, “An explainable deepfake detection framework on a novel unconstrained dataset,” Complex & Intelligent Systems, vol. 9, no. 4, pp. 4425–4437, 2023.
[21] F. Khalid, A. Javed, H. Ilyas, and A. Irtaza, “DFGNN: An interpretable and generalized graph neural network for deepfakes detection,” Expert Systems with Applications, vol. 222, p. 119843, 2023.
[22] A. H. Soudy et al., “Deepfake detection using convolutional vision transformers and convolutional neural networks,” Neural Computing and Applications, vol. 36, no. 31, pp. 19759–19775, 2024.
[23] M. Nawaz, A. Javed, and A. Irtaza, “ResNet-Swish-Dense54: A deep learning approach for deepfakes detection,” The Visual Computer, vol. 39, no. 12, pp. 6323–6344, 2023.
[24] R. R. Sekar, T. D. Rajkumar, and K. R. Anne, “Deep fake detection using an optimal deep learning model with multi-head attention-based feature extraction scheme,” The Visual Computer, pp. 1–18, 2024.
[25] Y. Patel et al., “An improved dense CNN architecture for deepfake image detection,” IEEE Access, vol. 11, pp. 22081–22095, 2023.
[26] A. Almestekawy, H. H. Zayed, and A. Taha, “Deepfake detection: Enhancing performance with spatiotemporal texture and deep learning feature fusion,” Egyptian Informatics Journal, vol. 27, p. 100535, 2024.
[27] N. U. Huda, A. Javed, K. Maswadi, A. Alhazmi, and R. Ashraf, “Fake-checker: A fusion of texture features and deep learning for deepfakes detection,” Multimedia Tools and Applications, vol. 83, no. 16, pp. 49013–49037, 2024.
[28] M. Ahmed, M. H. Sulaiman, A. J. Mohamad, and M. Rahman, “Gooseneck barnacle optimization algorithm: A novel nature-inspired optimization theory and application,” Mathematics and Computers in Simulation, vol. 218, pp. 248–265, 2024.
[29] A. J. Arunnehru et al., “Target object detection from unmanned aerial vehicle (UAV) images based on improved YOLO algorithm,” Electronics, vol. 11, no. 15, p. 2343, 2022.
[30] A. Alhussen et al., “XAI-RACapsNet: Relevance-aware capsule network-based breast cancer detection using mammography images via explainability O-net ROI segmentation,” Expert Systems with Applications, vol. 261, 2024.
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