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.

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“…Deepfake evaluations often involve scrutinizing metadata associated with digital files to identify anomalies or traces of manipulation, providing valuable insights into the authenticity of the content [10,14,15].…”

An Investigation of the Effectiveness of Deepfake Models and Tools

“…Although it briefly touches on the challenges, advances, and strategies related to deepfakes, the number of studies discussed is limited. The paper [70] conducts a systematic literature review (SLR) of deepfake creation and detection techniques, covering images and videos, and studies related to deepfake tweets, which are not commonly included in similar surveys.…”

“…These platforms allow for the mass distribution of deepfakes. To overcome ethical concerns, paper [70] proposes how to counter the threats from deepfake technology and to alleviate its impact. Figure 10 depicts the ethics of deepfake.…”

Fast Fake: Easy-to-Train Face Swap Model

“…In the era of the growing popularity of "Deep fake" technology [2][3][4][5][6], more and more attention is being paid to the possibilities of content manipulation. Although fascinating, the ability to swap one face for another carries enormous potential and numerous challenges.…”