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Techniques for creating and manipulating multimedia information have progressed to the point where they can now ensure a high degree of realism. DeepFake is a generative deep learning algorithm that creates or modifies face features in a superrealistic form, in which it is difficult to distinguish between real and fake features. This technology has greatly advanced and promotes a wide range of applications in TV channels, video game industries, and cinema, such as improving visual effects in movies, as well as a variety of criminal activities, such as misinformation generation by mimicking famous people. To identify and classify DeepFakes, research in DeepFake detection using deep neural networks (DNNs) has attracted increased interest. Basically, DeepFake is the regenerated media that is obtained by injecting or replacing some information within the DNN model. In this survey, we will summarize the DeepFake detection methods in face images and videos on the basis of their results, performance, methodology used and detection type. We will review the existing types of DeepFake creation techniques and sort them into five major categories. Generally, DeepFake models are trained on DeepFake datasets and tested with experiments. Moreover, we will summarize the available DeepFake dataset trends, focusing on their improvements. Additionally, the issue of how DeepFake detection aims to generate a generalized DeepFake detection model will be analyzed. Finally, the challenges related to DeepFake creation and detection will be discussed. We hope that the knowledge encompassed in this survey will accelerate the use of deep learning in face image and video DeepFake detection methods.

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This article presents a new method which embeds multiple data from multiple users in an encrypted image. Here, the data from several users is embedded into an encrypted image. Initially, the image is encrypted by the owner followed by embedding phase, where the encrypted image is divided into four sets. Two of them are used to embed the secret data, while others are remain unaltered. The secret data from multiple users are embedded into Most Significant Bit (MSB) of the encrypted image using their location maps. In the extraction phase, an individual owner can extract the data from the encrypted image using the assigned private key. Subsequently, in the image decryption and recovery phase, images can be recovered using the unaltered neighbor pixels. However, the secret image can be recovered losslessly using the encryption key only. The proposed scheme allows the extraction of the embedded information only for the authorized user out of several users without knowing the cover information. Various simulations have been made related to this, which show the high embedding rate and accuracy.