A Review of Image Processing Techniques for Deepfakes

Abstract

Deep learning is used to address a wide range of challenging issues including large data analysis, image processing, object detection, and autonomous control. In the same way, deep learning techniques are also used to develop software and techniques that pose a danger to privacy, democracy, and national security. Fake content in the form of images and videos using digital manipulation with artificial intelligence (AI) approaches has become widespread during the past few years. Deepfakes, in the form of audio, images, and videos, have become a major concern during the past few years. Complemented by artificial intelligence, deepfakes swap the face of one person with the other and generate hyper-realistic videos. Accompanying the speed of social media, deepfakes can immediately reach millions of people and can be very dangerous to make fake news, hoaxes, and fraud. Besides the well-known movie stars, politicians have been victims of deepfakes in the past, especially US presidents Barak Obama and Donald Trump, however, the public at large can be the target of deepfakes. To overcome the challenge of deepfake identification and mitigate its impact, large efforts have been carried out to devise novel methods to detect face manipulation. This study also discusses how to counter the threats from deepfake technology and alleviate its impact. The outcomes recommend that despite a serious threat to society, business, and political institutions, they can be combated through appropriate policies, regulation, individual actions, training, and education. In addition, the evolution of technology is desired for deepfake identification, content authentication, and deepfake prevention. Different studies have performed deepfake detection using machine learning and deep learning techniques such as support vector machine, random forest, multilayer perceptron, k-nearest neighbors, convolutional neural networks with and without long short-term memory, and other similar models. This study aims to highlight the recent research in deepfake images and video detection, such as deepfake creation, various detection algorithms on self-made datasets, and existing benchmark datasets.

Keywords: deep learning; deepfake; image processing; video altering.

Figure 6

Training and testing phases of FC-GAN [46].

Figure 1

Research article search and selection methodology.

Figure 2

Flow chart of paper selection methodology.

Figure 3

Examples of original and deepfake videos.

Figure 4

Deepfake generation process using encoder–decoder pair [40].

Figure 5

Architecture of DeepFaceLab from [41].

Figure 7

Types of deepfake videos and detection process.

Figure 8

Deepfake detection using CNN and LSTM [75].

Figure 9

Deepfake and original image: Original image (left), deepfake (right) [92].

Figure 10

Deepfake and GANprintR-processed deepfake: (a) Deepfake, (b) deepfake after GANprintR [93].