Advanced Detection and Mitigation Techniques for Deepfake Video: Leveraging AI to Safeguard Visual Media Integrity in Cybersecurity

Abstract: This study explores the multifaceted challenges posed by deepfake videos, drawing insights from case studies and interviews with journalists, cybersecurity experts, and victimized employees. It highlights the profound impact of deepfakes on journalism, where media professionals face increased responsibilities for verifying content authenticity. The findings reveal that current detection and mitigation methods are largely reactive, underscoring the need for proactive approaches involving AI, biometric analysis, and industry collaboration. The study also examines the organizational and personal impacts, emphasizing the psychological toll on individuals targeted by deepfakes and the varying levels of organizational preparedness. The urgent need for stronger regulatory measures is underscored, with experts calling for clearer legal frameworks to address the misuse of deepfake technology. Socio-cultural and ethical implications, such as the erosion of public trust and identity theft, highlight the broader societal impacts of deepfakes. The study concludes that a proactive, multi-layered response encompassing technological innovation, regulatory action, and public awareness is crucial to effectively mitigate the evolving threats posed by deepfake technology.

Keywords: Deepfake Technology; Journalism Integrity; Content Verification; Cybersecurity Threats; Digital Literacy; AI and Machine Learning; Identity Theft; Cross-Border Cooperation.