Deepfake detection using deep feature stacking and meta-learning

Abstract

Deepfake is a type of face manipulation technique using deep learning that allows for the replacement of faces in videos in a very realistic way. While this technology has many practical uses, if used maliciously, it can have a significant number of bad impacts on society, such as spreading fake news or cyberbullying. Therefore, the ability to detect deepfake has become a pressing need. This paper aims to address the problem of deepfake detection by identifying deepfake forgeries in video sequences. In this paper, a solution to the said problem is presented, which at first uses a stacking based ensemble approach, where features obtained from two popular deep learning models, namely Xception and EfficientNet-B7, are combined. Then by selecting a near-optimal subset of features using a ranking based approach, the final classification is performed to classify real and fake videos using a meta-learner, called multi-layer perceptron. In our experimentation, we have achieved an accuracy of 96.33% on Celeb-DF (V2) dataset and 98.00% on the FaceForensics++ dataset using the meta-learning model both of which are higher than the individual base models. Various types of experiments have been conducted to validate the robustness of the current method.