Deepfakes: temporal sequential analysis to detect face-swapped video clips using convolutional long short-term memory

Abstract. Deepfake (a bag of “deep learning” and “fake”) is a technique for human image synthesis based on artificial intelligence, i.e., to superimpose the existing (source) images or videos onto destination images or videos using neural networks (NNs). Deepfake enthusiasts have been using NNs to produce convincing face swaps. Deepfakes are a type of video or image forgery developed to spread misinformation, invade privacy, and mask the truth using advanced technologies such as trained algorithms, deep learning applications, and artificial intelligence. They have become a nuisance to social media users by publishing fake videos created by fusing a celebrity’s face over an explicit video. The impact of deepfakes is alarming, with politicians, senior corporate officers, and world leaders being targeted by nefarious actors. An approach to detect deepfake videos of politicians using temporal sequential frames is proposed. The proposed approach uses the forged video to extract the frames at the first level followed by a deep depth-based convolutional long short-term memory model to identify the fake frames at the second level. Also the proposed model is evaluated on our newly collected ground truth dataset of forged videos using source and destination video frames of famous politicians. Experimental results demonstrate the effectiveness of our method.