Deep Learning in Face Synthesis: A Survey on Deepfakes

Zhang, Teng; Deng, Lirui; Zhang, Liang; Dang, Xianglei

doi:10.1109/ccet50901.2020.9213159

Cited by 14 publications

(6 citation statements)

References 15 publications

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“…Additionally, for visual deepfakes (image and video), there are some more surveys (Zhang T. et al, 2020 ; Mirsky and Lee, 2021 ; Nguyen et al, 2021 ).…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

Audio deepfakes: A survey

Khanjani

Watson²,

Janeja³

2023

Front. Big Data

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A deepfake is content or material that is synthetically generated or manipulated using artificial intelligence (AI) methods, to be passed off as real and can include audio, video, image, and text synthesis. The key difference between manual editing and deepfakes is that deepfakes are AI generated or AI manipulated and closely resemble authentic artifacts. In some cases, deepfakes can be fabricated using AI-generated content in its entirety. Deepfakes have started to have a major impact on society with more generation mechanisms emerging everyday. This article makes a contribution in understanding the landscape of deepfakes, and their detection and generation methods. We evaluate various categories of deepfakes especially in audio. The purpose of this survey is to provide readers with a deeper understanding of (1) different deepfake categories; (2) how they could be created and detected; (3) more specifically, how audio deepfakes are created and detected in more detail, which is the main focus of this paper. We found that generative adversarial networks (GANs), convolutional neural networks (CNNs), and deep neural networks (DNNs) are common ways of creating and detecting deepfakes. In our evaluation of over 150 methods, we found that the majority of the focus is on video deepfakes, and, in particular, the generation of video deepfakes. We found that for text deepfakes, there are more generation methods but very few robust methods for detection, including fake news detection, which has become a controversial area of research because of the potential heavy overlaps with human generation of fake content. Our study reveals a clear need to research audio deepfakes and particularly detection of audio deepfakes. This survey has been conducted with a different perspective, compared to existing survey papers that mostly focus on just video and image deepfakes. This survey mainly focuses on audio deepfakes that are overlooked in most of the existing surveys. This article's most important contribution is to critically analyze and provide a unique source of audio deepfake research, mostly ranging from 2016 to 2021. To the best of our knowledge, this is the first survey focusing on audio deepfakes generation and detection in English.

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“…Additionally, for visual deepfakes (image and video), there are some more surveys (Zhang T. et al, 2020 ; Mirsky and Lee, 2021 ; Nguyen et al, 2021 ).…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

Audio deepfakes: A survey

Khanjani

Watson²,

Janeja³

2023

Front. Big Data

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“…Deepfake shave gained popularity as a result of the high quality of their altered videos and the accessibility of their applications to people with varying levels of computer expertise, from experts to beginners. Deep learning techniques are mostly used to construct these applications [12]. Therefore, many computer vision researchers have taken up the deepfake detection problem.…”

Section: Deepfake Creation and Detection Methodsmentioning

confidence: 99%

“…A computer was fed a large number of still images of one individual and video footage of another in order for the procedure to function. With matching expressions such as lip-synch and other motions, the software then created a new film (i.e., fake) [12]. Table 2 provides an overview of deepfake's tools and features.…”

Section: 60mentioning

confidence: 99%

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Analysis of the current state of deepfake techniques-creation and detection methods

Abu-Ein

Al-Hazaimeh

Dawood

et al. 2022

IJEECS

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Deep learning has effectively solved complicated challenges ranging from large data analytics to human level control and computer vision. However, deep learning has been used to produce software that threatens privacy, democracy, and national security. Deepfake is one of these new applications backed by deep learning. Fake images and movies created by Deepfake algorithms might be difficult for people to tell apart from real ones. This necessitates the development of tools that can automatically detect and evaluate the quality of digital visual media. This paper provides an overview of the algorithms and datasets used to build deepfakes, as well as the approaches presented to detect deepfakes to date. By reviewing the background of deepfakes methods, this paper provides a complete overview of deepfake approaches and promotes the creation of new and more robust strategies to deal with the increasingly complex deepfakes.

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“…[113] focuses on the newest works on deepfake field and describes how the related architectures work. [209] is another related survey that divides face image synthesis methods into three categories: face-reenactment, face-swap, and face-generation. The main focus of all these studies is on the current trends and advancements in the deepfake domain not facial attribute editing.…”

Section: Related Workmentioning

confidence: 99%