Abstract

Detecting fake faces has become a crucial endeavour within the realm of computer vision. The widespread availability of digital media has facilitated the creation and dissemination of deceptive and misleading content. A prominent strategy for identifying counterfeit faces employs advanced deep-learning methodologies that scrutinise both colour and textural attributes. This investigation is geared towards devising a method for discerning fake faces by leveraging the capabilities of convolutional neural networks (CNNs). These networks are trained to discriminate between authentic and forged images by discerning nuances in their colour characteristics. To achieve this, the MSU MFSD dataset will be harnessed, allowing for exploring colour textures and extracting facial traits across diverse colour channels, including RGB, HSV, and YCbCr.The proposed framework marks a notable stride in the realm of computer vision research, particularly given the prevalent employment of digital media, which has eased the generation and distribution of misleading or deceitful content. Developing dependable systems for identifying counterfeit faces holds immense potential in curtailing the proliferation of false information and upholding the integrity of digital media platforms.