Deep learning-driven classification ofdepth image-based rendering anddynamic face warping in deepfakealtered videos for fabricated news onsocial media

Deepfakes present a persistent challenge in a world driven by transparency and truth. This study introduce a new deepfake detection method based on Xception architecture. Deepfake is having some threats to the authenticity of digital information especially on social media platforms through manipulated content, which transpires fast within these networks and powerfully influences the public's opinion, reputational harm, and fuel of misinformation. This research introduced an Xception-based approach to deepfake detection. The architecture utilizes an efficient feature extraction CNN model that benefits from the improvements such as depthwise separable convolutions and is optimized to exploit subtle artifacts in typical deepfake media, such as inconsistencies in facial landmarks, lighting irregularities, and unnatural textures. It was trained and tested on an all-inclusive dataset of more than 500,000 frames comprising authentic and manipulated video content. The data preprocessing steps included face extraction and alignment and diversity augmentation of the data to make generalization better. The model was evaluated using various metrics and scored quite impressively at 99.69% accuracy, precision at 99.58%, recall at 99.80%, and with an F1 score at 99.69%. These results demonstrate that the model is correctly balanced to avoid false positives at a reasonable tradeoff for identifying deepfake content well enough to be deployable in real-world scenarios where content verification will prove critical. The Xception-based model also shown strong computational efficiency with the average inference time being 0.08 seconds per frame. With an AUC-ROC score of 0.9999, this efficiency combined proves that the model can significantly distinguish the real from the manipulated frames with near-perfect accuracy. More so, its robustness was tested under various conditions, including different resolutions and compressions. It achieved an accuracy of 99.65% on low-resolution frames and 98.1% on high-resolution frames, with an accuracy of greater than 90% even for highly compressed videos that ascertained its adaptability to the diverse media qualities characteristic of content usually found on social media. In summary, a deepfake detection framework based on Xception offers a powerful, accurate, and efficient solution to deepfake media, supporting the integrity and authenticity of digital content on social media. As possible future directions of this work, it could be considered to further explore the adversarial training for more robustness against emerging deep fake generation techniques and expanding the dataset with occluded and diverse samples. This research is therefore a valuable contribution to the area of deep fake detection, offering scalability of the tool to reduce misinformation and protect public trust in digital media.