The Analysis of Neural Network Models to Distinguish AI generated faces from Real faces

The rise of Artificial Intelligence (AI)-generated faces that are identical to actual ones is both a technological innovation and a major concern. While considering the implications, some of the existing security systems are unable to differentiate between a high-quality deep-fake and an actual intruder's face. For instance, the risks are quite high at an airport security checkpoint, where facial recognition is the first line of security against unauthorized entry. The primary concern here is how trustworthy the computer programs and algorithms will be in recognizing counterfeits among a plethora of actual and artificially generated faces. This necessitates the need to introduce machine learning approaches to differentiate between actual and fraudulent faces, particularly when AI-generated faces are involved. Effective artificial intelligence systems must be adaptive and change quickly in the face of more complex threats rather than simply recognizing them. AI-generated faces are becoming more convincing by the day, increasing the risk of their exploitation. This poses the need to ensure that the technology on which people rely should be robust and trustworthy in critical situations, whether it is a security checkpoint or an e-commerce site. In this perspective, this study has attempted to develop and implement Artificial Intelligence-powered solutions to detect the artificial faces while ensuring reliability for critical functions in an age where reality constantly blends with fiction.