A Survey on Enhanced Approaches for Cyber Security Challenges Based on Deep Fake Technology in Computing Networks

Abstract

With the help of artificial intelligence methods like as generative adversarial networks (GANs), deepfake technology has developed to produce incredibly lifelike but fraudulent audio, video, and image content. This technology presents significant cybersecurity risks, including as identity theft, social engineering, and public opinion manipulation, even while it has uses in creative media and entertainment. This study explores public awareness and views of deepfake concerns, focusing on people's attitudes toward potential remedies and their level of knowledge regarding potential misuse. An online poll was included in the mixed-methods approach to collect quantitative information on public awareness, perceived risks, and views on technical or regulatory actions. The findings indicate that respondents are generally aware of deepfakes and are particularly concerned about the threats to their privacy, their confidence in digital information, and the possibility of malevolent usage in corporate or political contexts. Most participants support proactive measures to lessen these risks, such as public awareness campaigns, improved regulatory frameworks, and the creation of detecting technology. Findings show that in order to defend against deepfake dangers, lawmakers, software developers, and cybersecurity experts must act quickly to coordinate their reaction. This study adds to the expanding corpus of research on the effects of deepfakes by highlighting the significance of multi-stakeholder cooperation and educated public engagement in addressing this changing cybersecurity threat.

Keywords: Cybersecurity, Deepfake Technology, Generative Adversarial Networks (GANs), Artificial Intelligence (AI), Digital Media, Identity Theft, Social Engineering, National Security, Misinformation, Detection Algorithms, Public Awareness, Mitigation Strategies, Fraud Political.