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

作　　者：Shraddha Suratkar Sayali Bhiungade Jui Pitale Komal Soni Tushar Badgujar Faruk Kazi 

机构地区：[1]Department of Electrical Engineering,Veermata Jijabai Technological Institute,An autonomous Institute,affiliated to Mumbai University,Mumbai,India

出　　处：《Journal of Control and Decision》2023年第2期198-214,共17页控制与决策学报（英文）

摘　　要：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 Memoryand 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.

关 键 词：Deep-FAKES Convolution Neural Network(CNN) Generator Adversarial Network(GAN) Auto encoders Recurrent Neural Network(RNN) Long Short-Term Memory(LSTM) 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]