The paper "Artificial and Deceitful Faces Detection Using Machine Learning" by Balusamy Nachiappan, N Rajkumar, C Vijji, and A Mohanraj explores the use of machine learning, specifically Convolutional Neural Networks (CNNs), to detect artificial and fraudulent faces. The authors highlight the limitations of traditional authentication methods like PIN and password, which are vulnerable to attacks due to their finite length. They propose a multi-modal boundary face recognition system that combines complex visual features from the latest facial recognition models with a custom CNN for enhanced authentication and extraction capabilities. The system is designed to be robust against image-based face recognition systems and can identify faces based on their geometry and material, not just appearance. The paper reviews existing literature on deep learning techniques, including GANs, and discusses the challenges of detecting covert manipulation techniques. It introduces a deep learning system that leverages the "uncanny valley" phenomenon to identify fake and fraudulent faces. The system employs multi-task learning to predict the future state of a face, enhancing its effectiveness compared to traditional methods that rely on handcrafted features. The method involves a face recognition module that creates realistic facial expressions, face detection using block searching windows, pre-processing to reduce noise and improve recognition rate, feature extraction using Gabor wavelets, and a CNN-based fake face detection system. The authors use a dataset of 30,000 real and fake faces from the CELEBAHQ dataset to train and test their model, demonstrating high accuracy in distinguishing between real and fake faces. The experimental setup and result analysis section details the data collection process, the effectiveness of the proposed method, and the performance metrics used to evaluate the system. The results show that the proposed method can accurately distinguish between real and fake faces, even when GANs generate realistic-looking faces. The authors conclude that their CNN-based approach is effective in identifying artificial and fraudulent faces, contributing to the security and reliability of facial recognition systems.The paper "Artificial and Deceitful Faces Detection Using Machine Learning" by Balusamy Nachiappan, N Rajkumar, C Vijji, and A Mohanraj explores the use of machine learning, specifically Convolutional Neural Networks (CNNs), to detect artificial and fraudulent faces. The authors highlight the limitations of traditional authentication methods like PIN and password, which are vulnerable to attacks due to their finite length. They propose a multi-modal boundary face recognition system that combines complex visual features from the latest facial recognition models with a custom CNN for enhanced authentication and extraction capabilities. The system is designed to be robust against image-based face recognition systems and can identify faces based on their geometry and material, not just appearance. The paper reviews existing literature on deep learning techniques, including GANs, and discusses the challenges of detecting covert manipulation techniques. It introduces a deep learning system that leverages the "uncanny valley" phenomenon to identify fake and fraudulent faces. The system employs multi-task learning to predict the future state of a face, enhancing its effectiveness compared to traditional methods that rely on handcrafted features. The method involves a face recognition module that creates realistic facial expressions, face detection using block searching windows, pre-processing to reduce noise and improve recognition rate, feature extraction using Gabor wavelets, and a CNN-based fake face detection system. The authors use a dataset of 30,000 real and fake faces from the CELEBAHQ dataset to train and test their model, demonstrating high accuracy in distinguishing between real and fake faces. The experimental setup and result analysis section details the data collection process, the effectiveness of the proposed method, and the performance metrics used to evaluate the system. The results show that the proposed method can accurately distinguish between real and fake faces, even when GANs generate realistic-looking faces. The authors conclude that their CNN-based approach is effective in identifying artificial and fraudulent faces, contributing to the security and reliability of facial recognition systems.