Integrating Deepfake Detection into Cybersecurity Curriculum

Chi, Hongmei; Maduakor, Udochi; Alò, Richard A.; Williams, Eleason A.

doi:10.1007/978-3-030-63128-4_45

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…In this scenario, a speech signal is produced as an auto-regressive process, given a collection of speech characteristics, including frequency (Hong et al, 2021), spectral envelope, and excitation signal. Nevertheless, parametric TTS synthesis yields fewer natural-sounding outcomes than concatenative TTS synthesis (Chi et al, 2020).…”

Section: Creating a Fake Speech And Fake Speech Detectionmentioning

confidence: 99%

Deepfake detection using deep learning methods: A systematic and comprehensive review

Heidari,

Jafari Navimipour,

Dag

et al. 2023

WIREs Data Min & Knowl

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Deep Learning (DL) has been effectively utilized in various complicated challenges in healthcare, industry, and academia for various purposes, including thyroid diagnosis, lung nodule recognition, computer vision, large data analytics, and human‐level control. Nevertheless, developments in digital technology have been used to produce software that poses a threat to democracy, national security, and confidentiality. Deepfake is one of those DL‐powered apps that has lately surfaced. So, deepfake systems can create fake images primarily by replacement of scenes or images, movies, and sounds that humans cannot tell apart from real ones. Various technologies have brought the capacity to change a synthetic speech, image, or video to our fingers. Furthermore, video and image frauds are now so convincing that it is hard to distinguish between false and authentic content with the naked eye. It might result in various issues and ranging from deceiving public opinion to using doctored evidence in a court. For such considerations, it is critical to have technologies that can assist us in discerning reality. This study gives a complete assessment of the literature on deepfake detection strategies using DL‐based algorithms. We categorize deepfake detection methods in this work based on their applications, which include video detection, image detection, audio detection, and hybrid multimedia detection. The objective of this paper is to give the reader a better knowledge of (1) how deepfakes are generated and identified, (2) the latest developments and breakthroughs in this realm, (3) weaknesses of existing security methods, and (4) areas requiring more investigation and consideration. The results suggest that the Conventional Neural Networks (CNN) methodology is the most often employed DL method in publications. According to research, the majority of the articles are on the subject of video deepfake detection. The majority of the articles focused on enhancing only one parameter, with the accuracy parameter receiving the most attention.This article is categorized under: Technologies > Machine Learning Algorithmic Development > Multimedia Application Areas > Science and Technology

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Section: Creating a Fake Speech And Fake Speech Detectionmentioning

confidence: 99%

Deepfake detection using deep learning methods: A systematic and comprehensive review

Heidari,

Jafari Navimipour,

Dag

et al. 2023

WIREs Data Min & Knowl

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show abstract

“…In cybersecurity, the deepfake is a growing threat [35]. One of the most well-known algorithms for generating deepfake content is generative adversarial networks (GANs) [36]. GANs are a combination of two artificial neural networks (ANNs).…”

Section: Deepfakementioning

confidence: 99%

A Study on the Psychology of Social Engineering-Based Cyberattacks and Existing Countermeasures

2022

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As cybersecurity strategies become more robust and challenging, cybercriminals are mutating cyberattacks to be more evasive. Recent studies have highlighted the use of social engineering by criminals to exploit the human factor in an organization’s security architecture. Social engineering attacks exploit specific human attributes and psychology to bypass technical security measures for malicious acts. Social engineering is becoming a pervasive approach used for compromising individuals and organizations (is relatively more convenient to compromise a human compared to discovering a vulnerability in the security system). Social engineering-based cyberattacks are extremely difficult to counter as they do not follow specific patterns or approaches for conducting an attack, making them highly effective, efficient, easy, and obscure approaches for compromising any organization. To counter such attacks, a better understanding of the attack tactics is highly essential. Hence, this paper provides an in-depth analysis of the approaches used to conduct social engineering-based cyberattacks. This study discusses human vulnerabilities employed by criminals in recent security breaches. Further, the paper highlights the existing approaches, including machine learning-based methods, to counter social engineering-based cyberattacks.

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“…This also means that one could be portrayed in a deepfake doing or saying something they did not do without their consent, as exemplified by the many instances of deepfake pornography [ 6 ]. Deepfakes threaten organizations as they risk privacy and security [ 7 ]. Well-made deepfakes can erode public trust in government [ 8 ] and public figures [ 5 ] and disturb political stability [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Perception and deception: Exploring individual responses to deepfakes across different modalities

Ahmed,

Chua

2023

Heliyon

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Integrating Deepfake Detection into Cybersecurity Curriculum

Cited by 6 publications

References 10 publications

Deepfake detection using deep learning methods: A systematic and comprehensive review

Deepfake detection using deep learning methods: A systematic and comprehensive review

A Study on the Psychology of Social Engineering-Based Cyberattacks and Existing Countermeasures

Perception and deception: Exploring individual responses to deepfakes across different modalities

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