Optifake: optical flow extraction for deepfake detection using ensemble learning technique

Artificial images and recordings are broad on the web via different media channels such as blogs, YouTube videos, etc. These manipulated and synthesized images tend to steal the identity of individuals and majorly contribute to establishing societal disruptions such as theft, political errors, social engineering, disinformation attacks and reputation fraud. These fake visual objects gradually came to be known as deep fakes. Different deep learning techniques are used to generate deepfake images which go unnoticed by human eyes. It is essential to develop a defense mechanism that can stop the common people from being manipulated and harnessed. The objective of this work is to develop an ensemble deep learning-based system that can differentiate between fake and real images. With the use of the recommended optical flow technique, a novel approach is proposed that extracts the apparent motion of image pixels which gives more accurate results compared to other state-of-the-art. FaceForensics + + dataset is used to test the extraction algorithms and ensemble model which fetched an accuracy of 86.02% for the DeepFake subset and 85.7% for the FaceSwap subset of the dataset. To the best knowledge, no one has completely used the ensemble model- OptiFake on the optical flow derived frames, highlighting a research gap in the field of deepfake detection.