DeepFakes: Detecting Forged and Synthetic Media Content Using Machine Learning

Sm Zobaed; Fazle Rabby; Istiaq Hossain; Ekram Hossain; Sazib Hasan; Asif Karim; Khan Md. Hasib

doi:10.1007/978-3-030-88040-8_7

DeepFakes: Detecting Forged and Synthetic Media Content Using Machine Learning

Sm Zobaed, Fazle Rabby, Istiaq Hossain, Ekram Hossain, Sazib Hasan, Asif Karim, Khan Md. Hasib

Higher Degree by Research (HDR) Cohort

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

The rapid advancement in deep learning makes the differentiation of authentic and manipulated facial images and video clips unprecedentedly harder. The underlying technology of manipulating facial appearances through deep generative approaches, enunciated as DeepFake that have emerged recently by promoting a vast number of malicious face manipulation applications. Subsequently, the need of other sort of techniques that can assess the integrity of digital visual content is indisputable to reduce the impact of the creations of DeepFake. A large body of research that are performed on DeepFake creation and detection create a scope of pushing each other beyond the current status. This study presents challenges, research trends, and directions related to DeepFake creation and detection techniques by reviewing the notable research in the DeepFake domain to facilitate the development of more robust approaches that could deal with the more advance DeepFake in future.

Original language	English
Title of host publication	Artificial Intelligence in Cyber Security: Impact and Implications
Subtitle of host publication	Security Challenges, Technical and Ethical Issues, Forensic Investigative Challenges
Editors	Reza Montasari, Hamid Jahankhani
Place of Publication	Cham, Switzerland
Publisher	Springer Nature
Chapter	7
Pages	177-201
Number of pages	25
Edition	1
ISBN (Electronic)	978-3-030-88040-8
ISBN (Print)	978-3-030-88039-2, 978-3-030-88042-2
DOIs	https://doi.org/10.1007/978-3-030-88040-8_7
Publication status	Published - 2021

Publication series

Name	Advanced Sciences and Technologies for Security Applications
ISSN (Print)	1613-5113
ISSN (Electronic)	2363-9466

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10.1007/978-3-030-88040-8_7

Cite this

Zobaed, S., Rabby, F., Hossain, I., Hossain, E., Hasan, S., Karim, A., & Md. Hasib, K. (2021). DeepFakes: Detecting Forged and Synthetic Media Content Using Machine Learning. In R. Montasari, & H. Jahankhani (Eds.), Artificial Intelligence in Cyber Security: Impact and Implications: Security Challenges, Technical and Ethical Issues, Forensic Investigative Challenges (1 ed., pp. 177-201). (Advanced Sciences and Technologies for Security Applications). Springer Nature. https://doi.org/10.1007/978-3-030-88040-8_7

Zobaed, Sm ; Rabby, Fazle ; Hossain, Istiaq et al. / DeepFakes : Detecting Forged and Synthetic Media Content Using Machine Learning. Artificial Intelligence in Cyber Security: Impact and Implications: Security Challenges, Technical and Ethical Issues, Forensic Investigative Challenges. editor / Reza Montasari ; Hamid Jahankhani. 1. ed. Cham, Switzerland : Springer Nature, 2021. pp. 177-201 (Advanced Sciences and Technologies for Security Applications).

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abstract = "The rapid advancement in deep learning makes the differentiation of authentic and manipulated facial images and video clips unprecedentedly harder. The underlying technology of manipulating facial appearances through deep generative approaches, enunciated as DeepFake that have emerged recently by promoting a vast number of malicious face manipulation applications. Subsequently, the need of other sort of techniques that can assess the integrity of digital visual content is indisputable to reduce the impact of the creations of DeepFake. A large body of research that are performed on DeepFake creation and detection create a scope of pushing each other beyond the current status. This study presents challenges, research trends, and directions related to DeepFake creation and detection techniques by reviewing the notable research in the DeepFake domain to facilitate the development of more robust approaches that could deal with the more advance DeepFake in future.",

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Zobaed, S, Rabby, F, Hossain, I, Hossain, E, Hasan, S, Karim, A & Md. Hasib, K 2021, DeepFakes: Detecting Forged and Synthetic Media Content Using Machine Learning. in R Montasari & H Jahankhani (eds), Artificial Intelligence in Cyber Security: Impact and Implications: Security Challenges, Technical and Ethical Issues, Forensic Investigative Challenges. 1 edn, Advanced Sciences and Technologies for Security Applications, Springer Nature, Cham, Switzerland, pp. 177-201. https://doi.org/10.1007/978-3-030-88040-8_7

DeepFakes: Detecting Forged and Synthetic Media Content Using Machine Learning. / Zobaed, Sm; Rabby, Fazle; Hossain, Istiaq et al.
Artificial Intelligence in Cyber Security: Impact and Implications: Security Challenges, Technical and Ethical Issues, Forensic Investigative Challenges. ed. / Reza Montasari; Hamid Jahankhani. 1. ed. Cham, Switzerland: Springer Nature, 2021. p. 177-201 (Advanced Sciences and Technologies for Security Applications).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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AU - Zobaed, Sm

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AU - Hossain, Ekram

AU - Hasan, Sazib

AU - Karim, Asif

AU - Md. Hasib, Khan

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AB - The rapid advancement in deep learning makes the differentiation of authentic and manipulated facial images and video clips unprecedentedly harder. The underlying technology of manipulating facial appearances through deep generative approaches, enunciated as DeepFake that have emerged recently by promoting a vast number of malicious face manipulation applications. Subsequently, the need of other sort of techniques that can assess the integrity of digital visual content is indisputable to reduce the impact of the creations of DeepFake. A large body of research that are performed on DeepFake creation and detection create a scope of pushing each other beyond the current status. This study presents challenges, research trends, and directions related to DeepFake creation and detection techniques by reviewing the notable research in the DeepFake domain to facilitate the development of more robust approaches that could deal with the more advance DeepFake in future.

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Zobaed S, Rabby F, Hossain I, Hossain E, Hasan S, Karim A et al. DeepFakes: Detecting Forged and Synthetic Media Content Using Machine Learning. In Montasari R, Jahankhani H, editors, Artificial Intelligence in Cyber Security: Impact and Implications: Security Challenges, Technical and Ethical Issues, Forensic Investigative Challenges. 1 ed. Cham, Switzerland: Springer Nature. 2021. p. 177-201. (Advanced Sciences and Technologies for Security Applications). doi: 10.1007/978-3-030-88040-8_7

DeepFakes: Detecting Forged and Synthetic Media Content Using Machine Learning

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