Paper Title
Differentiating Between AI generated Faces and Human Faces

Abstract
AI-generated faces are synthetic faces created by artificial intelligence that can have various applications and benefits but also pose serious risks and challenges. This paper addresses the problem of how to detect and differentiate between AI-generated faces and human faces, and how to protect the authenticity and integrity of digital media. The paper reviews the existing methods and challenges for face detection and differentiation and proposes a novel and robust method that integrates Generative Adversarial Networks (GANs) and ensem- ble methods. The proposed approach leverages the power of GANs to generate realistic human-like faces and combines it with ensemble methods to enhance accuracy and robustness in differentiating between real and AI-generated faces. By training the GAN on a diverse dataset of human faces and incorporating ensemble classifiers that specialize in various aspects of face analysis, including facial landmarks, texture analysis, and GAN- specific artifacts, the system achieves a high level of accuracy in distinguishing between real and AI-generated faces. The paper evaluates the performance of the proposed method using multiple datasets, including both publicly available datasets and custom- generated datasets, to assess its generalizability and robustness across different scenarios. Performance metrics such as accuracy, precision, recall, and F1 score are used to measure the effective- ness of the method in accurately identifying AI-generated faces. Furthermore, the paper discusses the limitations and challenges of the proposed method, including potential biases in the training data, scalability issues with large-scale deployment, and ethical considerations related to the use of AI-generated content. Future research directions are also explored, including the integration of advanced deep learning techniques, such as attention mechanisms and reinforcement learning, to further improve the accuracy and reliability of AI-generated face detection systems. Overall, this paper contributes to the ongoing research in AI ethics and digital media integrity by proposing a robust and effective method for detecting and differentiating between AI-generated faces and human faces, thereby addressing the growing concerns surrounding the authenticity of digital content. Keywords - Deepfake, Machine Learning, AI Generated Faces, Face Detection, Face Differentiation, GAN, Ensemble Learn- Ing.