Journal of Nonlinear Analysis and Optimization Vol. 15, Issue. 1, No.12 : 2024 ISSN : 1906-9685
AI SIMULATED FAUX IMAGE DETECTION SYSTEM USING DEEP LEARNING
ABSTRACT – Although biometric technology is essential for identifying people, thieves are always evolving to avoid being caught. We are using a state-of-the-art method called Deep Texture Features extraction from photos to tackle this problem. Building a Convolutional Neural Network (CNN) is our method for efficiently utilizing this technology. Known as LBPNET, this CNN-based model emphasizes the use of the Local Binary Pattern (LBP) methodology for feature extraction, making it stand out as a cuttingedge approach in the industry. To counter the spread of fake face photos in identification systems, we train the machine learning model to understand LBP descriptors taken from images. Our technique promises to improve the accuracy and dependability of facial recognition systems by integrating CNN technology with LBP descriptors, hence reducing the risk associated with fraudulent modifications to physical and psychological traits.
KEYWORDS: Deep textures, CNN, NLBPNet, LBPNet, LBP descriptor images.
