Detecting Deepfakes and Forged Videos Using Deep Learning
Abstract: Over just a few years, methods to manipulate videos have become so sophistica- ted that even someone without much expertise or computational resources can forge videos inseparable from pristine ones to the human eye. These methods can for instance insert a person in a video or manipulate their lip movements to make them say anything of the manipulator’s liking. Though there exist harm- less and constructive uses of these technologies, it is not hard to imagine the harm they could cause if put in the wrong hands. This report presents a model to detect forged manipulated videos, more specifically those where faces have been manipulated. Four kinds of manipu- lation videos were taken into consideration: FaceSwap, DeepFakes, Face2Face and Neural Textures. The model proposed consists of a feature extraction CNN followed by an LSTM network. The FaceForensics++ dataset was used, as well as the associated benchmark. The model, though not competing with the state- of-the-art detectors, was able to classify videos with an accuracy higher than or close to that of several models in the benchmark.
AT THIS PAGE YOU CAN DOWNLOAD THE WHOLE ESSAY. (follow the link to the next page)