A Survey on Multimodal Deepfake and Detection Techniques

Li Zeyu; Zhang Xuhong; Pu Yuwen; Wu Yiming; Ji Shouling

doi:10.7544/issn1000-1239.202111119

Journal of Computer Research and Development > 2023 > 60(6): 1396-1416. > DOI: 10.7544/issn1000-1239.202111119 CSTR: 32373.14.issn1000-1239.202111119

Li Zeyu, Zhang Xuhong, Pu Yuwen, Wu Yiming, Ji Shouling. A Survey on Multimodal Deepfake and Detection Techniques[J]. Journal of Computer Research and Development, 2023, 60(6): 1396-1416. DOI: 10.7544/issn1000-1239.202111119

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A Survey on Multimodal Deepfake and Detection Techniques

1.
College of Computer Science and Technology, Zhejiang University, Hangzhou 310007
2.
College of Control Science and Engineering, Zhejiang University, Hangzhou 310007

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Author Bio:
Li Zeyu: born in 1999. Master candidate. His main research interests include AI security and computer vision

Zhang Xuhong: born in 1988. PhD, associate professor. Member of CCF. His main research interests include AI security, data driven software and system security, big data systems and analytics

Pu Yuwen: born in 1993. PhD. His main research interests include privacy computing and AI security

Wu Yiming: born in 1996. PhD candidate. Her main research interests include data driven security, black industry mining, and cybercrime research

Ji Shouling: born in 1986. PhD, professor, PhD supervisor. Senior member of CCF. His main research interests include data-driven security and privacy, AI security, big data mining and analytics
Received Date: November 11, 2021
Revised Date: August 28, 2022
Available Online: March 19, 2023

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Abstract

Abstract

With the application of all kinds of deep learning generation models in various fields, the authenticity of their generated multimedia files has become increasingly difficult to distinguish, therefore, deepfake technology has been born and developed. Utilizing deep learning related techniques, the deepfake technology can tamper with the facial identity information, expressions, and body movements in videos or pictures, and generate fake voice of a specific person. Since 2018, when Deepfakes sparked a wave of face swapping on social networks, a large number of deepfake methods have been proposed, which had demonstrated their potential applications in education, entertainment, and some other fields. But at the same time, the negative impact of deepfake on public opinion, judicial and criminal investigations, etc. can not be ignored. As a consequence, more and more countermeasures have been proposed to prevent deepfake from being utilized by the criminals, such as the detection of deepfake and watermark. Firstly, a review and summary of deepfake technologies of different modal types and corresponding detection technologies are carried out, and the existing researches are analyzed and classified according to the research purpose and research method. Secondly, the video and audio datasets widely used in the recent studies are summarized. Finally, the opportunities and challenges for future development in this field are discussed.
- deepfake,
- deepfake detection,
- deep learning,
- face replacement,
- generative adversarial network

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References (181)

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