Detecting Deepfakes in Healthcare: A Review and Proposed Solution
Rahaf Adam Alnuaimi, Moatsum Alawida, Maryam Almarzooqi, Dima Talal Alhalabi, Hamzah Ali Alamaireh · 2024
The widespread use of Deepfake technology presents serious challenges to the integrity and security of healthcare systems. This study provides a detailed review of Deepfake de-tection methods designed particularly for the healthcare domain. We investigate the present methodologies, privacy concerns, and data security risks associated with Deepfakes in medical contexts. Based on a thorough analysis of the current literature, we offer a new approach framework for healthcare-specific Deepfake detection that incorporates powerful machine learning algorithms and privacy-preserving strategies. We highlight research gaps, challenges, and future possibilities for research in this crucial domain, emphasizing the need for reliable, ethical, and adaptive Deepfake detection systems in healthcare.