Impact of Learning Rate on CNN-Based Deepfake Detection

This research examines how CNN-based deepfake detection is affected by different fixed learning rates (0.0001, 0.0002, and 0.0005). To train a lightweight CNN model, video frames were taken, modified, and normalized using the Celeb-DF (v2) dataset. The model was trained utilizing three epochs with a batch size of four, employing the Adam optimization algorithm for enhanced performance. The performance of the model was evaluated through the analysis of training accuracy, validation accuracy, training loss, and validation loss. These metrics provided a comprehensive assessment of the model's effectiveness in both learning from the training data and generalizing to the validation dataset. The results of the study indicate that a learning rate of 0.0005 leads to instability and a tendency toward overfitting, while a learning rate of 0.0001 is associated with underfitting due to slow convergence. In contrast, an optimal learning rate of 0.0002 achieves a balanced performance, yielding the highest validation accuracy at 86% and the lowest validation loss of 0.35. This highlights the importance of selecting an appropriate learning rate to enhance model performance effectively. With possible uses in enhancing GAN-based image classification systems, this study focuses on the influence of learning rate selection on deepfake detection.