Enhancing Deepfake Detection Through Facial Pattern Recognition and Transfer Learning

The swift progress of artificial intelligence and deep learning technologies, detecting deep fake content in such content has been accelerated, while at the same time, great concerns as to the authenticity of digital media have been instilled. Added to this is deep fake technology that enables manufacturing of altered images, videos and sound, which could propagate misinformation or defamatory content about a public figure, including political entity, harmful to their reputation. The increasing threat of deepfakes emphasizes the need for robust detection techniques, the more adept deepfake technology becomes. The input of this study is to offer a complete survey of current deepfake identification approaches, which include machine learning mainly based on convolutional and recurrent neural networks. It evaluates different datasets and detection techniques to give the best performance of separating the real and facial lines content. Moreover, it describes preceding challenges and outlines promising future research directions to positively change approaches to detection accuracy via the combination of hybrid deep learning models. This review serves as the principal attempt to facilitate informed decision making in the quickly advancing area of deepfake identification thus as a contributive measure towards effective risk mitigation strategies.