Deep Fake Face Detection Using Deep Learning Tech with LSTM

Abstract

The fabrication of extremely life like spoof films and pictures that are getting harder to tell apart from actual content is now possible because to the quick advancement of deep fake technology. A number of industries, including cybersecurity, politics, and journalism, are greatly impacted by the widespread use of deepfakes, which seriously jeopardizes the accuracy of digital media. In computer vision, machine learning, and digital forensics, detecting deepfakes has emerged as a crucial topic for study and development. An outline of the most recent cutting-edge methods and difficulties in deep fake detection is given in this abstract. In this article, we go over the fundamental ideas behind deepfake creation and investigate the many approaches used to spot and stop the spread of fake news. Methods include sophisticated machine learning algorithms trained on enormous datasets of real and fake media, as well as conventional forensic investigation. We explore the principal characteristics and artifacts that differentiate authentic video from deepfakes, such as disparities in audio-visual synchronization, aberrant eye movements, and inconsistent facial emotions. Convolutional neural networks (CNNs) and generative adversarial networks (GANs), two deep learning frameworks, have been used by researchers to create sophisticated detection models that can recognize minute modifications in multimedia information. The fast developments in deep fake generating techniques, however, continue to exceed efforts in detection and mitigation, making deep fake detection a daunting problem. The issue is made worse by the democratization of deepfake technology and its accessibility to non-experts, which calls for creative solutions and multidisciplinary cooperation to counter this expanding danger. Keywords: convolutional neural network,, generative adversarial network, deep fake ,long short term memory,