Significant advances in deep learning have obtained hallmark accuracy rates for various computer vision applications. However, advances in deep generative models have also led to the generation of very realistic fake content, also known as deepfakes, causing a threat to privacy, democracy, and national security. Most of the current deepfake detection methods are deemed as a binary classification problem in distinguishing authentic images or videos from fake ones using two-class convolutional neural networks (CNNs). These methods rely on identifying visual anomalies, temporal or color inconsistencies generated by deep generative models. Nonetheless, their effectiveness diminishes notably when assessed across datasets. To address this challenges, This dissertation introduces both passive and proactive approaches for detecting deepfakes based on facial forgery. These methods aim to narrow the disparities observed during deepfake detection while simultaneously improving performance and imperceptibility.