Building a Simple Deepfake Video Detector

Building a simple deepfake video detector involves several key steps. First, you’ll need to gather a diverse dataset of authentic and deepfake videos to train your model. Techniques such as convolutional neural networks (CNNs) can be employed for feature extraction from frames. Next, you should preprocess the video data, extracting frames at regular intervals and normalizing them for consistent input size. After that, you can use a machine learning framework like TensorFlow or PyTorch to create a CNN model, training it to differentiate between real and manipulated content. Implement data augmentation to enhance the robustness of your model against overfitting. Finally, evaluate your model’s performance using metrics like accuracy, precision, and recall, and iteratively refine it based on results. Once trained, you can deploy the model to analyze new videos, flagging potential deepfakes for further review.