Deep-fake video detection approaches using convolutional – recurrent neural networks

Abstract

Deep-Fake is an emerging technology used in synthetic media which manipulates individuals in existing images and videos with someone else's likeness. This paper presents the comparative study of different deep neural networks employed for Deep-Fake video detection. In the model, the features from the training data are extracted with the intended Convolution Neural Network model to form feature vectors which are further analysed using a dense layer, a Long Short-Term Memory and Gated Recurrent by adopting transfer learning with fine tuning for training the models. The model is evaluated to detect Artificial Intelligence based Deep fakes images and videos using benchmark datasets. Comparative analysis shows that the detections are majorly biased towards domain of the dataset but there is a noteworthy improvement in the model performance parameters by using Transfer Learning whereas Convolutional- Recurrent Neural Network has benefits in sequence detection.