Deepfake Generation and Detection: A Benchmark and Survey
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866918379061772288 |
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| author | Pei, Gan Zhang, Jiangning Hu, Menghan Zhang, Zhenyu Wang, Chengjie Wu, Yunsheng Zhai, Guangtao Yang, Jian Tao, Dacheng |
| author_facet | Pei, Gan Zhang, Jiangning Hu, Menghan Zhang, Zhenyu Wang, Chengjie Wu, Yunsheng Zhai, Guangtao Yang, Jian Tao, Dacheng |
| contents | Deepfake is a technology dedicated to creating highly realistic facial images and videos under specific conditions, which has significant application potential in fields such as entertainment, movie production, digital human creation, to name a few. With the advancements in deep learning, techniques primarily represented by Variational Autoencoders and Generative Adversarial Networks have achieved impressive generation results. More recently, the emergence of diffusion models with powerful generation capabilities has sparked a renewed wave of research. In addition to deepfake generation, corresponding detection technologies continuously evolve to regulate the potential misuse of deepfakes, such as for privacy invasion and phishing attacks. This survey comprehensively reviews the latest developments in deepfake generation and detection, summarizing and analyzing current state-of-the-arts in this rapidly evolving field. We first unify task definitions, comprehensively introduce datasets and metrics, and discuss developing technologies. Then, we discuss the development of several related sub-fields and focus on researching four representative deepfake fields: face swapping, face reenactment, talking face generation, and facial attribute editing, as well as forgery detection. Subsequently, we comprehensively benchmark representative methods on popular datasets for each field, fully evaluating the latest and influential published works. Finally, we analyze challenges and future research directions of the discussed fields. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_17881 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deepfake Generation and Detection: A Benchmark and Survey Pei, Gan Zhang, Jiangning Hu, Menghan Zhang, Zhenyu Wang, Chengjie Wu, Yunsheng Zhai, Guangtao Yang, Jian Tao, Dacheng Computer Vision and Pattern Recognition Deepfake is a technology dedicated to creating highly realistic facial images and videos under specific conditions, which has significant application potential in fields such as entertainment, movie production, digital human creation, to name a few. With the advancements in deep learning, techniques primarily represented by Variational Autoencoders and Generative Adversarial Networks have achieved impressive generation results. More recently, the emergence of diffusion models with powerful generation capabilities has sparked a renewed wave of research. In addition to deepfake generation, corresponding detection technologies continuously evolve to regulate the potential misuse of deepfakes, such as for privacy invasion and phishing attacks. This survey comprehensively reviews the latest developments in deepfake generation and detection, summarizing and analyzing current state-of-the-arts in this rapidly evolving field. We first unify task definitions, comprehensively introduce datasets and metrics, and discuss developing technologies. Then, we discuss the development of several related sub-fields and focus on researching four representative deepfake fields: face swapping, face reenactment, talking face generation, and facial attribute editing, as well as forgery detection. Subsequently, we comprehensively benchmark representative methods on popular datasets for each field, fully evaluating the latest and influential published works. Finally, we analyze challenges and future research directions of the discussed fields. |
| title | Deepfake Generation and Detection: A Benchmark and Survey |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.17881 |