• Gosangi Alekhya
• , A.Mary Sowjanya
• J.Aruna Devi

Abstract Biometric technology are helpful in identifying people today, but criminals alter their look, behaviour, and psychological makeup to trick identification systems. We are employing a novel method called Deep Texture Features extraction from photos to solve this challenge, followed by the construction of a machine learning model using the CNN (Convolution Neural Networks) algorithm. This method is also known as LBPNet or NLBPNet since it relies so heavily on the LBP (Local Binary Pattern) algorithm for features extraction. LBPNET, a machine learning convolution neural network, is the name of the network we created for this research to identify fraudulent face photographs. Here, we will first extract LBP from the photos, and then we will train the convolution neural network on the LBP descriptor images to produce the training model. Every time a new test image is uploaded, the training model will use that image to determine if the test image contains fraudulent images or not.

LBP,CNN,LBPNET, biometric

"Fake Image Detection using deep learning and local binary patterns", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 9, page no.b303-b308, September-2022, Available :http://www.jetir.org/papers/JETIR2209137.pdf

"Fake Image Detection using deep learning and local binary patterns", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 9, page no. ppb303-b308, September-2022, Available at : http://www.jetir.org/papers/JETIR2209137.pdf