The DOI matches its Crossref record; 30 of the 34 references it cites were found in a registry or an index.
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
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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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In the citing paper
“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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Citations
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Celeb-DF: A Large-Scale Challenging Dataset for DeepFake Forensics
Li, Y., Yang, X., Sun, P., Qi, H., & Lyu, S. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00327 · Cited by 1,846 · cited 2×
“In a recent work [27], we present a new large-scale challenging DeepFake video dataset, Celeb-DF, which contains 5, 639 high-quality DeepFake videos of celebrities generated using improved synthesis process.”
BackgroundMethodSelf-citationDeepfake Video Detection Using Recurrent Neural Networks
Guera, D., & Delp, E. J. (2018) · 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2018.8639163 · Cited by 1,285
Exposing Deep Fakes Using Inconsistent Head Poses
Yang, X., Li, Y., & Lyu, S. (2019) · ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2019.8683164 · Cited by 1,196
Self-citationIn Ictu Oculi: Exposing AI Created Fake Videos by Detecting Eye Blinking
Li, Y., Chang, M.-C., & Lyu, S. (2018) · 2018 IEEE International Workshop on Information Forensics and Security (WIFS) · doi:10.1109/wifs.2018.8630787 · Cited by 1,089
Self-citationCapsule-forensics: Using Capsule Networks to Detect Forged Images and Videos
Nguyen, H. H., Yamagishi, J., & Echizen, I. (2019) · ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2019.8682602 · Cited by 951
Research works printed but not linked: 1
- goodfellow, 2014, Generative adversarial nets, Advances in Neural Information Processing Systems (NIPS)
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Deep Forgery Detection Based on Staged Differential Feature Enhancement
Long, M., & Lin, Y. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-9946-9_4
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Li, Z., Tang, W., Gao, S., Wang, Y., & Wang, S. (2025) · International Conference on Computing and Artificial Intelligence (ICCAI) · doi:10.1109/iccai66501.2025.00109
A Comparative Analysis of Advanced Deep Learning Techniques for Detecting Deepfake Audio
Arya, G., Tyagi, S. S., Nayak, T., & Sharma, T. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-7131-1_6
The impacts of deepfakes on journalism: Risks, opportunities, and changes in news organizations
Raemy, P., & Puppis, M. (2025) · Communications · doi:10.1515/commun-2024-0211
Singh, R., & Agarwal, R. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-032-03751-0_34
The Face of the Algorithmic Image: Early Film Theory and Deepfakes
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Sharma, A., Paserlanka, D. B., Lankadasu, N. V. Y., Sharma, S., & Malik, A. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-0877-7.ch010
Effect of Spoof Speech on Forensic Voice Comparison Using Deep Speaker Embeddings
Alsalihi, M. H., & Sztahó, D. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-07956-5_21
Effect of Compression on the Detection of Deepfake Videos with Facial Features
Jadhav, C., Ratnaparkhe, V. R., & Kumar, R. (2026) · Lecture notes in networks and systems · doi:10.1007/978-981-96-9932-2_39
Forensic Applications of Generative AI in Image Reconstruction
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Face Forgery Detection Based on ResNet Networks
Li, Y. (2025) · IEEE International Conference on Civil Aviation Safety and Information Technology (ICCASIT) · doi:10.1109/iccasit66611.2025.11348796
Spot the Fake: Vision Transformers in the Fight Against DeepFakes
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Diffusion Based Face Generation via Image Editing and Image Morphing
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Методы обнаружения дипфейков в видеоконференциях в реальном времени
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Artificial Intelligence and the Death of the Author:
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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
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