Example Concept: Deepfake detection typically involves convolutional neural networks (CNNs) that analyze frames for pixel-level discrepancies, such as unnatural eye blinking or facial distortions. The model compares these features against known patterns of authentic videos to flag potential deepfakes. Additionally, temporal inconsistencies across frames can be detected using recurrent neural networks (RNNs) to assess motion and continuity.