Three-classification face manipulation detection using attention-based feature decomposition

Yungui, Cao; Chen, Jiazhen; Huang, Luqi; Huang, Tianqian; Ye, Feng

doi:10.1016/j.cose.2022.103024

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Cited by 3 publications

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Exploring the Landscape of Compressed DeepFakes: Generation, Dataset and Detection

Zubair,

Hakak

2025

Neurocomputing

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Exploring the Landscape of Compressed DeepFakes: Generation, Dataset and Detection

Zubair,

Hakak

2025

Neurocomputing

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A Lightweight CNN for Efficient Deepfake Detection of Low-resolution Images in Frequency Domain

Ravi

et al. 2024

2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE)

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Deepfake technology amidst health emergencies: Discussing challenges, exploring opportunities, and charting strategic future paths (Preprint)

UNISA

2024

Preprint

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BACKGROUND This study proposes strategic directions for future research and policymaking, fostering a nuanced understanding of the dynamic interplay between deepfake technology and health emergencies. It provides recommendations for policymakers and stakeholders to effectively navigate challenges and harness opportunities presented by deepfake technology. OBJECTIVE _The primary objective is to provide insights into the applications of deepfake technology during health emergencies, analysing its effects on information dissemination, public trust, and crisis management_ METHODS This paper employs a comprehensive approach to investigate the impact of deepfake technology in the context of health emergencies. The study examines challenges, opportunities, and strategic pathways for the future. RESULTS Challenges identified include the potential for misinformation, erosion of trust in health communication, and disruptions to established rapid response mechanisms. Opportunities exist to utilise deepfake technology for medical training and health awareness campaigns. CONCLUSIONS The paper contributes original insights by evaluating current detection and prevention strategies, acknowledging advancements, and recognising limitations. It offers a comprehensive discussion on the broader implications of deepfakes for emergency health management.

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Three-classification face manipulation detection using attention-based feature decomposition

Cited by 3 publications

References 15 publications

Exploring the Landscape of Compressed DeepFakes: Generation, Dataset and Detection

Exploring the Landscape of Compressed DeepFakes: Generation, Dataset and Detection

A Lightweight CNN for Efficient Deepfake Detection of Low-resolution Images in Frequency Domain

Deepfake technology amidst health emergencies: Discussing challenges, exploring opportunities, and charting strategic future paths (Preprint)

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