Forward Thinking of Detecting Indiscernible Video Counterfeits

Deep fake technology has spread to digital media integrity, which can easily generate extremely realistic but fake videos for misinformation, identity theft, and propaganda. In this paper, we present an end-to-end deep fake video detection framework with advanced convolutional neural networks and temporal analysis, achieving an accuracy of 94.7%, precision of 93.2%, and recall of 95.1% on the test set, which are significantly superior to the state of-the-art methods proposed in previous works. Our method not only overcomes cross-dataset generalization but also balances the efficiency for real-time application and robustness against adversarial attacks. The model performs impressively, with an AUC-ROC of 0.967, and stays reliable even when video quality or compression levels vary. It also generalizes well, scoring 89.3% on DFDC and 91.8% on CelebDF-v2, showing strong performance across different datasets. Therefore, this work has paved the way for trustworthy deep fake detection systems protecting the integrity of digital media on social platforms, supporting journalistic reporting, and legal procedures.