Exploring the Role of Loss Functions in Deepfake Detection

With deepfakes becoming more common, it's increasingly difficult to separate real content from manipulated ones. As vast amounts of data are generated daily, the need for effective deepfake detection becomes increasingly critical. We aim to apply a basic CNN model to check if an image is real or fake. We examine how various loss functions, including binary cross-entropy, focal loss, and hinge loss, impact training and detection performance. The models were evaluated on a dataset of 190,335 images with a focus on accuracy and loss. Binary Cross-Entropy performs well on balanced datasets, Focal Loss shows superior performance in imbalanced datasets, and Hinge Loss provides robust margin-based classification. Our findings provide insights into the optimal loss functions for enhancing the performance of deepfake detection models. At 92.70% training accuracy and a 0.0118 training loss, the Focal Loss model emerged as the best, providing a solid foundation for future advancements in deepfake detection.