The Creation, editing, and production of high- quality photos have been greatly facilitated by advances in image processing and machine learning algorithms. Unfortunately, attackers can use these technologies to generate images that look convincingly real but are fraudulent; this is often done to harm others or try and circumvent image detection techniques and deceive image recognition classifiers. To carry out face-morphing attacks, fraudsters have access to readily available digital modification tools. This could result in several unlawful behaviours and harm to the actual person’s personal life. In order to prevent the spread of such modified photographs and protect the innocent, it is necessary to identify them before they appear on different platforms. Researchers use Fake Face Image Detection to determine whether an image is now altered, which exonerates fraudsters who upload such phoney pictures. This research makes use of convolutional neural networks and machine learning (CNN). This proposed method can produce satisfying results with an average accuracy of over 89.4% by first proposing a convolutional Neural Network (CNN)-based method to identify fraudulent face photos.