Generation and Detection of Artificial Facial Images

Artificial Intelligence (AI), machine learning, and deep learning have revolutionized various real-world applications. This paper presents a technique to generate artificial facial images and proposes deep-learning approaches to distinguish between real and artificial facial images. The objective is to develop an effective method for discerning genuine faces from manipulated ones. The proposed models utilize a dataset comprising both real and artificial images and employ neural networks-based deep learning algorithms. Social media platforms have become predominant sources of information, yet not all shared content is genuine; photographs can be altered, leading to deepfakes as a significant threat for spreading misinformation. Therefore, there is an urgent need to develop models capable of detecting deepfakes. This paper introduces a deepfake detection framework utilizing convolutional neural networks and the publicly available 140K Real and Fake Faces dataset to accurately identify deepfakes in images.