Abstract

The Deepfake software has developed as a potent tool for creating extremely realistic but deceptive graphics, presenting serious safety and security issues. As fake information algorithms advance, differentiating both actual and modified images gets more difficult This study addresses this concern by using a powerful analysis algorithm that uses neural networks to distinguish between real and manipulated images Specifically, three convolutional neural networks— XceptionNet, InceptionV3, and EfficientNetB0— is used for this task. The simulation is performed using a set of data with the changed facial characteristics, including eyes, mouth, mid-face, and nose First-order techniques such as shrinkage and standardization are used to provide a model the performance is improved. This technology analyzes images as "real" or "fake" and detects changing facial feature areas. The model performance is measured using various metrics which includes accuracy, precision, recall, and confusion matrices, enabling appropriate and efficient depth feature detection

References

• Lu, Y. and Ebrahimi, T., 2024. Assessment framework for deepfake detection in real-world situations. EURASIP Journal on Image and Video Processing, 2024(1), p.6.
• Tarchi, P., Lanini, M.C., Frassineti, L. and Lanatà, A., 2023. Real and Deepfake Face Recognition: An EEG Study on Cognitive and Emotive Implications. Brain Sciences, 13(9), p.1233
• Malik, A., Kuribayashi, M., Abdullahi, S.M. and Khan, A.N., 2022. DeepFake detection for human face images and videos: A survey. Ieee Access, 10, pp.18757-18775
• Guarnera, L., Giudice, O., Guarnera, F., Ortis, A., Puglisi, G., Paratore, A., Bui, L.M., Fontani, M., Coccomini, D.A., Caldelli, R. and Falchi, F., 2022. The face deepfake detection challenge. Journal of Imaging, 8(10), p.263.
• Wolter, M., Blanke, F., Heese, R. and Garcke, J., 2022. Wavelet-packets for deepfake image analysis and detection. Machine Learning, 111(11), pp.4295-4327.
• Raza, A., Munir, K. and Almutairi, M., 2022. A novel deep learning approach for deepfake image detection. Applied Sciences, 12(19), p.9820.
• Taeb, M. and Chi, H., 2022. Comparison of deepfake detection techniques through deep learning. Journal of Cybersecurity and Privacy, 2(1), pp.89-106.
• Suganthi, S.T., Ayoobkhan, M.U.A., Bacanin, N., Venkatachalam, K., Štěpán, H. and Pavel, T., 2022. Deep learning model for deep fake face recognition and detection. PeerJ Computer Science, 8, p.e881.
• Almars, A.M., 2021. Deepfakes detection techniques using deep learning: a survey. Journal of Computer and Communications, 9(05), pp.20-35.
• Kim, E. and Cho, S., 2021. Exposing fake faces through deep neural networks combining content and trace feature extractors. IEEE Access, 9, pp.123493-123503.