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
Cites
GAN（Generative Adversarial Nets）
淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 20,507
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Progressive Growing of GANs for Improved Quality, Stability, and Variation
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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
Deep Fakes: A Looming Challenge for Privacy, Democracy, and National Security
Chesney, R., & Citron, D. K. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3213954 · Cited by 880 · cited 1×
“Well-crafted DeepFake videos can create illusions of a person’s presence and activities that do not occur in reality, which can lead to serious political, social, financial, and legal consequences [7].”
BackgroundLearning Rich Features for Image Manipulation Detection
Zhou, P., Han, X., Morariu, V. I., & Davis, L. S. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00116 · Cited by 855
Exploiting Visual Artifacts to Expose Deepfakes and Face Manipulations
Matern, F., Riess, C., & Stamminger, M. (2019) · 2019 IEEE Winter Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw.2019.00020 · Cited by 824
Exposing DeepFake Videos By Detecting Face Warping Artifacts
Li, Y., & Lyu, S. (2018) · arXiv · doi:10.48550/arxiv.1811.00656 · Cited by 785
Self-citationTwo-Stream Neural Networks for Tampered Face Detection
Zhou, P., Han, X., Morariu, V. I., & Davis, L. S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.229 · Cited by 700
Multi-task Learning for Detecting and Segmenting Manipulated Facial Images and Videos
Nguyen, H. H., Fang, F., Yamagishi, J., & Echizen, I. (2019) · 2019 IEEE 10th International Conference on Biometrics Theory, Applications and Systems (BTAS) · doi:10.1109/btas46853.2019.9185974 · Cited by 549
DeepFakes: a New Threat to Face Recognition? Assessment and Detection
Korshunov, P., & Marcel, S. (2018) · arXiv · doi:10.48550/arxiv.1812.08685 · Cited by 497
Hybrid LSTM and Encoder–Decoder Architecture for Detection of Image Forgeries
Bappy, J. H., Simons, C., Nataraj, L., Manjunath, B. S., & Roy–Chowdhury, A. K. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2895466 · Cited by 493
Recurrent convolutional strategies for face manipulation detection in videos
Sabir, E., Cheng, J., Jaiswal, A., AbdAlmageed, W., Masi, I., & Natarajan, P. (2019) · arXiv · doi:10.48550/arxiv.1905.00582 · Cited by 443
(2019) · Cited by 415
The Deepfake Detection Challenge (DFDC) Preview Dataset
Dolhansky, B., Howes, R., Pflaum, B., Baram, N., & Ferrer, C. C. (2019) · arXiv · doi:10.48550/arxiv.1910.08854 · Cited by 413
Deep voice 3: scaling text-to-speech with convolutional sequence learning
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Use of a Capsule Network to Detect Fake Images and VideosPreprint
Nguyen, H. H., Yamagishi, J., & Echizen, I. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.12467 · Cited by 178
In Ictu Oculi: Exposing AI Generated Fake Face Videos by Detecting Eye BlinkingPreprint
Li, Y., Chang, M., & Lyu, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.02877 · Cited by 176
Self-citationImage Forgery Localization based on Multi-Scale Convolutional Neural Networks
Liu, Y., Guan, Q., Zhao, X., & Cao, Y. (2018) · ACM Workshop on Information Hiding and Multimedia Security (IH&MMSec) · doi:10.1145/3206004.3206010 · Cited by 106
Farid, H. (2008) · Scientific American · doi:10.1038/scientificamerican0608-66 · Cited by 77
Detecting AI-Synthesized Speech Using Bispectral Analysis
AlBadawy, E. A., Lyu, S., & Farid, H. (2019) · Cited by 36
Self-citationHiding Faces in Plain Sight: Disrupting AI Face Synthesis with Adversarial PerturbationsPreprint
Li, Y., Yang, X., Wu, B., & Lyu, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.09288 · Cited by 32
Self-citationGu, Y., & Kang, Y. (2018) · Interspeech (USB) · doi:10.21437/interspeech.2018-1506 · Cited by 16
Recurrent-Convolution Approach to DeepFake Detection - State-Of-Art Results on FaceForensics++Preprint
Sabir, E., Cheng, J., Jaiswal, A., AbdAlmageed, W., Masi, I., & Natarajan, P. (2019) · arXiv (Cornell University) · Cited by 5
Research works printed but not linked: 1
- goodfellow, 2014, Generative adversarial nets, Advances in Neural Information Processing Systems (NIPS)
Cited by
Sorted by citations
Deepfake Detection: A Systematic Literature Review
Rana, M. S., Nobi, M. N., Murali, B., & Sung, A. H. (2022) · IEEE Access · doi:10.1109/access.2022.3154404 · Cited by 573
Deep Learning for Deepfakes Creation and Detection: A Survey
Nguyen, T. T., Nguyen, Q. V. H., Nguyen, D. T., Nguyen, D. T., Huynh-The, T., Nahavandi, S., Nguyen, T. T., Pham, Q.-V., & Nguyen, C. M. (2019) · arXiv · doi:10.48550/arxiv.1909.11573 · Cited by 464
Deep learning for deepfakes creation and detection: A survey
Nguyen, T. T., Nguyen, Q. V. H., Nguyen, D. T., Nguyen, D. T., Huynh-The, T., Nahavandi, S., Nguyen, T. T., Pham, Q.-V., & Nguyen, C. M. (2022) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2022.103525 · Cited by 464
“Thus far, there have been numerous methods proposed to detect deepfakes [19]–[23].”
MethodEnd-to-End Reconstruction-Classification Learning for Face Forgery Detection
Cao, J., Ma, C., Yao, T., Chen, S., Ding, S., & Yang, X. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00408 · Cited by 428
Deepfake detection by human crowds, machines, and machine-informed crowds
Groh, M., Epstein, Z., Firestone, C., & Picard, R. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2110013119 · Cited by 248
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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