Deepfake Detection and Classification of Images from Video: A Review of Features, Techniques, and Challenges

Dennis Lucky Tuanwi Bale; Laud Charles Ochei; Chidiebere Ugwu

doi:doi:10.11648/j.ijiis.20241302.11

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Deepfake Detection and Classification of Images from Video: A Review of Features, Techniques, and Challenges

Dennis Lucky Tuanwi Bale, Laud Charles Ochei^*, Chidiebere Ugwu

Published in International Journal of Intelligent Information Systems (Volume 13, Issue 2)

Received: 5 March 2024 Accepted: 18 March 2024 Published: 2 April 2024

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Abstract

The proliferation of deepfake technology poses significant challenges to the integrity and authenticity of visual content in videos, raising concerns about misinformation and deceptive practices. In this paper, we present a comprehensive review of features, techniques, and challenges related to the detection and classification of deepfake images extracted from videos. Existing literature has explored various approaches, including feature-based methods, machine learning algorithms, and deep learning techniques, to mitigate the adverse effects of deepfake content. However, challenges persist, such as the evolution of deepfake generation methods and the scarcity of diverse datasets for training detection models. To address these issues, this paper reviews related work on approaches for deepfake image detection and classification and synthesises these approaches into four categories - feature extraction, machine learning, and deep learning. The findings underscore the importance of continued research efforts in this domain to combat the harmful effects of deepfake technology on society. This study provides recommendations for future research directions, emphasizing the significance of proactive measures in mitigating the spread of manipulated visual content.

Published in	International Journal of Intelligent Information Systems (Volume 13, Issue 2)
DOI	10.11648/j.ijiis.20241302.11
Page(s)	20-28
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Deepfake, Detection, Classification, Video, Image, Features, Techniques

References

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APA Style

Bale, D. L. T., Ochei, L. C., Ugwu, C. (2024). Deepfake Detection and Classification of Images from Video: A Review of Features, Techniques, and Challenges. International Journal of Intelligent Information Systems, 13(2), 20-28. https://doi.org/10.11648/j.ijiis.20241302.11

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ACS Style

Bale, D. L. T.; Ochei, L. C.; Ugwu, C. Deepfake Detection and Classification of Images from Video: A Review of Features, Techniques, and Challenges. Int. J. Intell. Inf. Syst. 2024, 13(2), 20-28. doi: 10.11648/j.ijiis.20241302.11

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AMA Style

Bale DLT, Ochei LC, Ugwu C. Deepfake Detection and Classification of Images from Video: A Review of Features, Techniques, and Challenges. Int J Intell Inf Syst. 2024;13(2):20-28. doi: 10.11648/j.ijiis.20241302.11

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@article{10.11648/j.ijiis.20241302.11,
  author = {Dennis Lucky Tuanwi Bale and Laud Charles Ochei and Chidiebere Ugwu},
  title = {Deepfake Detection and Classification of Images from Video: A Review of Features, Techniques, and Challenges
},
  journal = {International Journal of Intelligent Information Systems},
  volume = {13},
  number = {2},
  pages = {20-28},
  doi = {10.11648/j.ijiis.20241302.11},
  url = {https://doi.org/10.11648/j.ijiis.20241302.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20241302.11},
  abstract = {The proliferation of deepfake technology poses significant challenges to the integrity and authenticity of visual content in videos, raising concerns about misinformation and deceptive practices. In this paper, we present a comprehensive review of features, techniques, and challenges related to the detection and classification of deepfake images extracted from videos. Existing literature has explored various approaches, including feature-based methods, machine learning algorithms, and deep learning techniques, to mitigate the adverse effects of deepfake content. However, challenges persist, such as the evolution of deepfake generation methods and the scarcity of diverse datasets for training detection models. To address these issues, this paper reviews related work on approaches for deepfake image detection and classification and synthesises these approaches into four categories - feature extraction, machine learning, and deep learning. The findings underscore the importance of continued research efforts in this domain to combat the harmful effects of deepfake technology on society. This study provides recommendations for future research directions, emphasizing the significance of proactive measures in mitigating the spread of manipulated visual content.
},
 year = {2024}
}

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TY  - JOUR
T1  - Deepfake Detection and Classification of Images from Video: A Review of Features, Techniques, and Challenges

AU  - Dennis Lucky Tuanwi Bale
AU  - Laud Charles Ochei
AU  - Chidiebere Ugwu
Y1  - 2024/04/02
PY  - 2024
N1  - https://doi.org/10.11648/j.ijiis.20241302.11
DO  - 10.11648/j.ijiis.20241302.11
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 20
EP  - 28
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.20241302.11
AB  - The proliferation of deepfake technology poses significant challenges to the integrity and authenticity of visual content in videos, raising concerns about misinformation and deceptive practices. In this paper, we present a comprehensive review of features, techniques, and challenges related to the detection and classification of deepfake images extracted from videos. Existing literature has explored various approaches, including feature-based methods, machine learning algorithms, and deep learning techniques, to mitigate the adverse effects of deepfake content. However, challenges persist, such as the evolution of deepfake generation methods and the scarcity of diverse datasets for training detection models. To address these issues, this paper reviews related work on approaches for deepfake image detection and classification and synthesises these approaches into four categories - feature extraction, machine learning, and deep learning. The findings underscore the importance of continued research efforts in this domain to combat the harmful effects of deepfake technology on society. This study provides recommendations for future research directions, emphasizing the significance of proactive measures in mitigating the spread of manipulated visual content.

VL  - 13
IS  - 2
ER  -

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Author Information

Dennis Lucky Tuanwi Bale

Department of Computer Science, University of Port Harcourt, Port Harcourt, Nigeria

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Laud Charles Ochei

Department of Computer Science, University of Port Harcourt, Port Harcourt, Nigeria

Contact Email
Chidiebere Ugwu

Department of Computer Science, University of Port Harcourt, Port Harcourt, Nigeria

Contact Email

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Plain Text BibTeX RIS

APA Style

Bale, D. L. T., Ochei, L. C., Ugwu, C. (2024). Deepfake Detection and Classification of Images from Video: A Review of Features, Techniques, and Challenges. International Journal of Intelligent Information Systems, 13(2), 20-28. https://doi.org/10.11648/j.ijiis.20241302.11

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ACS Style

Bale, D. L. T.; Ochei, L. C.; Ugwu, C. Deepfake Detection and Classification of Images from Video: A Review of Features, Techniques, and Challenges. Int. J. Intell. Inf. Syst. 2024, 13(2), 20-28. doi: 10.11648/j.ijiis.20241302.11

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AMA Style

Bale DLT, Ochei LC, Ugwu C. Deepfake Detection and Classification of Images from Video: A Review of Features, Techniques, and Challenges. Int J Intell Inf Syst. 2024;13(2):20-28. doi: 10.11648/j.ijiis.20241302.11

Copy | Download

@article{10.11648/j.ijiis.20241302.11,
  author = {Dennis Lucky Tuanwi Bale and Laud Charles Ochei and Chidiebere Ugwu},
  title = {Deepfake Detection and Classification of Images from Video: A Review of Features, Techniques, and Challenges
},
  journal = {International Journal of Intelligent Information Systems},
  volume = {13},
  number = {2},
  pages = {20-28},
  doi = {10.11648/j.ijiis.20241302.11},
  url = {https://doi.org/10.11648/j.ijiis.20241302.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20241302.11},
  abstract = {The proliferation of deepfake technology poses significant challenges to the integrity and authenticity of visual content in videos, raising concerns about misinformation and deceptive practices. In this paper, we present a comprehensive review of features, techniques, and challenges related to the detection and classification of deepfake images extracted from videos. Existing literature has explored various approaches, including feature-based methods, machine learning algorithms, and deep learning techniques, to mitigate the adverse effects of deepfake content. However, challenges persist, such as the evolution of deepfake generation methods and the scarcity of diverse datasets for training detection models. To address these issues, this paper reviews related work on approaches for deepfake image detection and classification and synthesises these approaches into four categories - feature extraction, machine learning, and deep learning. The findings underscore the importance of continued research efforts in this domain to combat the harmful effects of deepfake technology on society. This study provides recommendations for future research directions, emphasizing the significance of proactive measures in mitigating the spread of manipulated visual content.
},
 year = {2024}
}

Copy | Download

TY  - JOUR
T1  - Deepfake Detection and Classification of Images from Video: A Review of Features, Techniques, and Challenges

AU  - Dennis Lucky Tuanwi Bale
AU  - Laud Charles Ochei
AU  - Chidiebere Ugwu
Y1  - 2024/04/02
PY  - 2024
N1  - https://doi.org/10.11648/j.ijiis.20241302.11
DO  - 10.11648/j.ijiis.20241302.11
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 20
EP  - 28
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.20241302.11
AB  - The proliferation of deepfake technology poses significant challenges to the integrity and authenticity of visual content in videos, raising concerns about misinformation and deceptive practices. In this paper, we present a comprehensive review of features, techniques, and challenges related to the detection and classification of deepfake images extracted from videos. Existing literature has explored various approaches, including feature-based methods, machine learning algorithms, and deep learning techniques, to mitigate the adverse effects of deepfake content. However, challenges persist, such as the evolution of deepfake generation methods and the scarcity of diverse datasets for training detection models. To address these issues, this paper reviews related work on approaches for deepfake image detection and classification and synthesises these approaches into four categories - feature extraction, machine learning, and deep learning. The findings underscore the importance of continued research efforts in this domain to combat the harmful effects of deepfake technology on society. This study provides recommendations for future research directions, emphasizing the significance of proactive measures in mitigating the spread of manipulated visual content.

VL  - 13
IS  - 2
ER  -

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