A Comparative Study: Deepfake Detection Using Deep-learning

In recent decades, we have seen significant advancement in fields like Artificial Intelligence, Machine Learning, and Deep Learning, resulting in the developing of new technologies such as deepfake. Deepfakes are a form of digital media that replaces one identity’s likeness with another or creates a synthetic personality: in the form of high-quality realistic fake video, image, or audio. Deepfakes can be helpful in education, art, activism, and self-expression; however, some subjects can use deepfakes to harm the portrayal of people, create pornographic content, and spread misleading information. High-quality deepfakes are easy to build but incredibly difficult to detect, creating a need to explore technologies which can be helpful in deepfake detection. Therefore, we present a comparative study of deep-learning models that can benefit deepfake detection. We have explored four deep-learning models, namely, VGG16, MobileNetV2, XceptionNet, and InceptionV3 and trained these models on the FaceForensics++ dataset. Finally, we evaluate the performance of these models for deepfake detection and conclude the study with our observations and future scope for improvement in this field.