Abstract

Fake face detection is an important task in the field of computer vision. The proliferation of digital media has made it easier for individuals to create and share misleading or deceptive content. One approach to fake face detection is through the use of deep learning techniques that analyze color and textual features. This study aims to detect fake faces by analyzing their color features using deep learning. It involves training a convolutional neural network (CNN) to differentiate between genuine and fake images based on variations in their color characteristics. The MSU MFSD dataset will be used in this paper to investigate color texture and extract facial features from various color channels such as RGB, HSV, and YCbCr. This proposed system is a significant advancement in computer vision research, particularly in light of the prevalent use of digital media, which has made it easier to generate and distribute deceptive or misleading content. Creating reliable fake face detection systems can play a vital role in combating the dissemination of false information and safeguarding the credibility of digital media.