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Deepfakes have become a modern challenge, making it easy to manipulate faces, voices, and even entire scenes in videos. When facial masks are involved—whether due to health, privacy, or deception—detecting deepfakes becomes even trickier. Building on recent research in masked face recognition, this work explores how advanced image processing and deep neural networks can be adapted to spot deepfakes, especially when faces are partially covered. The proposed approach combines facial region separation, robust augmentation, and targeted feature analysis to improve deepfake detection accuracy under real-world, masked conditions. Early experiments suggest this method makes a real difference for security and authentication systems grappling with both deepfakes and masks.