The Threat of Deep Fake Technology to Trusted Identity Management

Ali, Atif Mossad; Jadoon, Yasir Khan; Farid, Zulqarnain; Ahmad, Munir; Abid, Naseem; Alzoubi, Haitham M.; Alzoubi, Ali A.

doi:10.1109/iccr56254.2022.9995978

Cited by 8 publications

(3 citation statements)

References 26 publications

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“…Further, some works were left aside since they didn't fit the survey's scope in terms of application or architecture. In this context, (Rezende et al, 2017), for instance, employ a shallow model, that is, the Support Vector Machines (SVM) for classification purposes, while (Agarwal et al, 2021; Atif et al, 2022; Mohammad et al, 2021; Shruti & Hany, 2021; Shubham et al, 2021) presents different methods for fake image classification using distinct feature extraction techniques. Additionally, the work of (Birunda et al, 2022), which addresses deepfake detection using the Flood Fill algorithm, can be included in this list.…”

Section: Review Methodologymentioning

confidence: 99%

A review of deep learning‐based approaches for deepfake content detection

Passos,

Jodas,

Costa

et al. 2024

Expert Systems

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Recent advancements in deep learning generative models have raised concerns as they can create highly convincing counterfeit images and videos. This poses a threat to people's integrity and can lead to social instability. To address this issue, there is a pressing need to develop new computational models that can efficiently detect forged content and alert users to potential image and video manipulations. This paper presents a comprehensive review of recent studies for deepfake content detection using deep learning‐based approaches. We aim to broaden the state‐of‐the‐art research by systematically reviewing the different categories of fake content detection. Furthermore, we report the advantages and drawbacks of the examined works, and prescribe several future directions towards the issues and shortcomings still unsolved on deepfake detection.

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Section: Review Methodologymentioning

confidence: 99%

A review of deep learning‐based approaches for deepfake content detection

Passos,

Jodas,

Costa

et al. 2024

Expert Systems

View full text Add to dashboard Cite

show abstract

“…Further, some works were left aside since they didn't fit the survey's scope in terms of application or architecture. In this context, Rezende et al [16], for instance, employs a shallow model, i.e., the Support Vector Machines (SVM) for classification purposes, while [17,18,19,20,21] presents different methods for fake image classification using distinct feature extraction techniques. Additionally, the work of Birunda et al [22], which addresses deepfake detection using the Flood Fill algorithm, can be included in this list.…”

Section: Selection Criteriamentioning

confidence: 99%

A Review of Deep Learning-based Approaches for Deepfake Content Detection

Passos,

Jodas,

Costa

et al. 2023

Preprint

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Recent advancements in deep learning generative models have raised concerns as they can create highly convincing counterfeit images and videos. This poses a threat to people's integrity and can lead to social instability. To address this issue, there is a pressing need to develop new computational models that can efficiently detect forged content and alert users to potential image and video manipulations. This paper presents a comprehensive review of recent studies for deepfake content detection using deep learning-based approaches. We aim to broaden the state-of-the-art research by systematically reviewing the different categories of fake content detection. Furthermore, we report the advantages and drawbacks of the examined works and future directions towards the issues and shortcomings still unsolved on deepfake detection.

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“…They employed a smartphone with a compromised camera to trick the tax invoice system with pre-generated deepfake identities. Another concerning aspect is its potential misuse in child predator scenarios [27], where the predator hides behind a virtual avatar, needing no resemblance to any existing individual. reenactment is a suitable form of deepfakes to spoof face biometrics systems implementing real-time challenge-response liveness detection mechanisms.…”

Section: Face Swapmentioning

confidence: 99%

DeepFake on Face and Expression Swap: A Review

Waseem,

Abu Bakar,

Ahmed

et al. 2023

IEEE Access

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Remarkable advances have been made in deep learning, leading to the emergence of highly realistic AIgenerated videos known as deepfakes. Deepfakes use generative models to manipulate facial features to create modified identities or expressions with impressive realism. These synthetic media creations can deceive, discredit, or blackmail individuals and threaten the integrity of the legal, political, and social systems. Consequently, researchers are actively developing techniques to detect deepfake content to preserve privacy and combat the dissemination of fabricated media. This article presents a comprehensive study examining existing methods of creating deepfake images and videos for face and expression replacement. In addition, it provides an overview of publicly available deepfake datasets for benchmarking, serving as important resources for training and evaluating deepfake detection systems. In addition, the study sheds light on the detection approaches used to identify deepfake face and expression swaps while discussing the challenges and issues involved. However, the focus of this study goes beyond identifying the existing barriers. It goes a step further by outlining future research directions and guiding future scientists to address the concerns that need to be addressed in deepfake detection methods. In this way, this paper aims to facilitate the development of robust and effective deepfake detection solutions for face and expression swaps, thereby contributing to ongoing efforts to protect the authenticity and trustworthiness of visual media.

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The Threat of Deep Fake Technology to Trusted Identity Management

Cited by 8 publications

References 26 publications

A review of deep learning‐based approaches for deepfake content detection

A review of deep learning‐based approaches for deepfake content detection

A Review of Deep Learning-based Approaches for Deepfake Content Detection

DeepFake on Face and Expression Swap: A Review

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