Detecting AI Face Fraud Detection Using CNN Based Deep Learning Algorithm

Abstract

The advancement of deep learning and generative models has led to the creation of highly realistic artificial images, including human faces. However, this progress has also raised concerns about potential misuse, such as using AI-generated faces for fraudulent activities. Addressing this issue, AI Face Fraud Detection has become a crucial area of focus for research and development. The primary objective is to develop an Artificial Intelligence system sophisticated enough to effectively discern true human images from AI-generated ones. The research's Background is the increased face-based fraud threat. Human animators can misuse instances of AI-generated faces to act as individuals with that face or create false identities. The AI Face Fraud Detection system is based on CNN, GAN advanced deep learning techniques to analyze and differentiate between true human faces and AI generated faces. By subjecting it to extensive training with a wide range of human images and AI-generated faces, the system is resistant and highly flexible to discern illegal activity. AI Face Fraud Detection involves key components like facial recognition algorithms, feature extraction techniques (CNN, LBP, HOG), and fraud detection models. Performance metrics are used to evaluate model effectiveness in distinguishing real human images from AI-generated ones. The proposed model achieves more than 90% as in all performance metrics terms. The impact of this research spans across multiple domains, including cyber security, digital forensics, and identity verification. By effectively identifying AI-generated faces, organizations can bolster their fraud prevention strategies, safeguard user identities, and foster trust in digital interactions. In summary, this study contributes to leveraging AI for enhancing security and authenticity in today's digital landscape.

Topics & Concepts