Deepfake videos: synthesis and detection techniques – a survey

Saif, Shahela; Tehseen, Samabia

doi:10.3233/jifs-210625

Cited by 5 publications

(4 citation statements)

References 30 publications

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“…Similarly, deepfake detection is a significant application of DL and ML that assists detect forgeries in media, including videos and photos. An amount of research has indeed been done on it, including a thorough study and implementation of many common algorithms (Albahar & Almalki, 2019; T. T. Nguyen, Nguyen, et al, 2019; Pashine et al, 2021; Saif & Tehseen, 2022; Shelke & Kasana, 2021; Swathi & Sk, 2021; Weerawardana & Fernando, 2021). So, DL has been recognized as an excellent method for detecting synthetic media in the deepfake realm.…”

Section: Relevant Reviewsmentioning

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: Relevant Reviewsmentioning

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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“…Dealing with deepfakes can be divided into two main approaches: techniques for deepfakes generation and techniques for deepfakes detection (Mirsky & Lee, 2022;Masood et al, 2022;Malik, Kuribayashi, Abdullahi, & Khan, 2022;Rana, Nobi, Murali, & Sung, 2022;Saif & Tehseen, 2022;Dagar & Vishwakarma, 2022). Since the emergence in 2014 of Generative Adversarial Networks (GANs) (I. J.…”

Section: Computer-generated Contents and Disinformationmentioning

confidence: 99%

“…Since the emergence in 2014 of Generative Adversarial Networks (GANs) (I. J. Goodfellow et al, 2014), there has been an explosion of work on deepfakes generation and, as a counterpart, deepfakes detection (Masood et al, 2022;Mirsky & Lee, 2022;Dagar & Vishwakarma, 2022;Saif & Tehseen, 2022;Tolosana, Vera-Rodriguez, Fierrez, Morales, & Ortega-Garcia, 2020).…”

Section: Computer-generated Contents and Disinformationmentioning

confidence: 99%

“…A full face manipulation involves the creation of an image of a non-existing face, some of the architectures for entire face generation are ProGAN (Karras, Aila, Laine, & Lehtinen, 2018) and StyleGAN (Karras, Laine, & Aila, 2021). There are multiple works to detect whether an image is artificial or real, and in recent years the trend has been towards the use of convolutional neural networks (CNNs) with attention mechanisms (Tolosana et al, 2020;Rana et al, 2022;Saif & Tehseen, 2022;Dagar & Vishwakarma, 2022). Partial face manipulation is the most common type of deepfake used to generate false information.…”

Section: Computer-generated Contents and Disinformationmentioning

confidence: 99%

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IBERIFIER Reports: Is the ‘AI toolbox for disinformation’ ready?

Montoro Montarroso,

Camacho,

Martín

et al. 2023

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This project has received funding from the European Commission under the call CEF-TC-2020-2 (European Digital Media Observatory)Reference: 2020-EU-IA-0252 Consortium DisclaimerAll information provided reflects the status of the IBERIFIER project at the time of writing and may be subject to change.Neither the IBERIFIER Consortium as a whole, nor any single party within the IBERIFIER Consortium warrant that the information contained in this document is capable of use, nor that the use of such information is free from risk. Neither the IBERIFIER Consortium as a whole, nor any single party within the IBERIFIER Consortium accepts any liability for loss or damage suffered by any person using the information.This document does not represent the opinion of the European Community, and the European Community is not responsible for any use that might be made of its content.

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Engaging with deepfakes: a meta-synthesis from the perspective of social shaping of technology theory

Vasist

Krishnan²

2022

INTR

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PurposeThis study aims to establish a comprehensive understanding of the intricacies of how individuals engage with deepfakes, focusing on limiting adverse effects and capitalizing on their benefits.Design/methodology/approachThis study conducted a meta-synthesis of qualitative studies on deepfakes, incorporating study-specific analysis followed by a cross-study synthesis.FindingsBased on the meta-synthesis, the study developed an integrated conceptual framework based on the perspectives from the social shaping of technology theory embedding deepfake-related assertions, motivations, the subtleties of digital platforms, and deepfake-related repercussions.Research limitations/implicationsThe study offers crucial insights into the evolving nature of deepfakes as a socio-technical phenomenon and the significance of platform dynamics in deepfake production. It enables researchers to comprehend the cascading effects of deepfakes and positions them to evaluate deepfake-related risks and associated mitigation mechanisms.Practical implicationsThe framework that emerges from the study illustrates the influence of platforms on the evolution of deepfakes and assists platform stakeholders in introducing effective platform governance structures to combat the relentless proliferation of deepfakes and their consequences, as well as providing guidance for governments and policymakers to collaborate with platform leaders to set guardrails for deepfake engagement.Originality/valueDeepfakes have been extensively contested for both their beneficial and negative applications and have been accused of heralding an imminent epistemic threat that has been downplayed by some quarters. This diversity of viewpoints necessitates a comprehensive understanding of the phenomenon. In responding to this call, this is one of the first to establish a comprehensive, theoretically informed perspective on how individuals produce, process, and engage with deepfakes through a meta-synthesis of qualitative literature on deepfakes.

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Deepfake videos: synthesis and detection techniques – a survey

Cited by 5 publications

References 30 publications

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

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

IBERIFIER Reports: Is the ‘AI toolbox for disinformation’ ready?

Engaging with deepfakes: a meta-synthesis from the perspective of social shaping of technology theory

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