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Deepfake Detection: Current Challenges and Next Steps
Open accessAbstract
High quality fake videos and audios generated by AI- algorithms (the deep fakes) have started to challenge the status of videos and audios as definitive evidence of events. In this paper, we highlight a few of these challenges and discuss the research opportunities in this direction.
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“The authors of many surveys (e.g., Lyu, 2020; Younus and Hasan, 2020; Deshmukh and Wankhade, 2021; Masood et al., 2023; Sandotra and Arora, 2024), argued that better (higher-quality, more up-to-date, and more standard) deepfake datasets are needed to develop more effective deepfake detection methods.”
From Deepfake: definitions, performance metrics and standards, datasets, and a meta-review (2024) · cites this paper as Lyu, 2020
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“The author tried to raise a deep concern about one of the critical disadvantages of the current DeepFake generation methods that cannot produce a fine mapping of color shades for hair with respect to the human face.”
From A Comprehensive Review of DeepFake Detection Using Advanced Machine Learning and Fusion Methods (2023) · cites this paper as [19]
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“Lyu (2020) and Masood et al. (2023) also suggested that we need to consider social media laundering effects in training data and improve the evaluation of datasets.”
From Deepfake: definitions, performance metrics and standards, datasets, and a meta-review (2024) · cites this paper as Lyu, 2020
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“some used a broader definition, e.g., Lyu (2020) defined deepfake as “high quality fake videos and audios generated by AI algorithms.””
From Deepfake: definitions, performance metrics and standards, datasets, and a meta-review (2024) · cites this paper as Lyu, 2020
In the citing paper
“traditional digital media forensics, which often rely on image metadata (14) that are not available for many of today’s most concerning deepfakes, which typically appear first on social media platforms stripped of such metadata (15, 16).”
From Deepfake detection by human crowds, machines, and machine-informed crowds (2022) · cites this paper as [16]
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Research works printed but not linked: 1
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Cited by
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Metadata
- Authors
- Lyu, S.
- Affiliations
- University at Albany, State University of New York (US)
- Corresponding author
- Lyu, Siwei
- ORCID
- Lyu, Siwei
- Venue
- 2020 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)
- Publisher
- IEEE
- Published
- 2020-07
- Pages
- 1-6
- OpenAlex
- W3012725859
- Cited by
- 234
- References
- 34
- Citations by year
- 2026: 18 · 2025: 40 · 2024: 54 · 2023: 54 · 2022: 40 · 2021: 23 · 2020: 5
- FWCI
- 9.41
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE International Conference on Multimedia & Expo Workshops (ICMEW) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- IEEE RESOURCE CENTERS
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Keywords
- deepfake detection, synthetic media, fake videos, AI-generated content, digital evidence, misinformation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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