REAL AND FAKE FACE DETECTION USING DEEP LEARNING AND STEAM LIT

Abstract

The proliferation of deep learning algorithms has led to a growing prevalence in the generation of very realistic
counterfeit faces. This presents a substantial risk in diverse applications, including security and authentication.
In order to address this problem, we suggest using a deep learning methodology to accurately distinguish
between genuine and counterfeit faces. Initially, we import the requisite libraries and proceed to load the dataset
including authentic as well as counterfeit facial photos. Subsequently, we do exploratory data analysis (EDA) in
order to get insights into the distribution of characteristics within the dataset. This encompasses the use of
picture rescaling and data augmentation approaches to improve the resilience of the model. Subsequently, we
use four distinct deep learning methods, including MobileNetV2, InceptionV3, DenseNet, and AntiSpoof, to
classify actual and false faces. Every algorithm has unique advantages and disadvantages, and we assess their
effectiveness by using diverse criteria like as accuracy, precision, recall, and F1-score. Ultimately, we assess the
effectiveness of the four algorithms and determine the optimal model for distinguishing between genuine and
counterfeit faces. The findings of our study show that deep learning is very successful in accurately
differentiating between authentic and counterfeit faces. Moreover, our suggested methodology may be used in
several contexts to safeguard against face-related fraudulent activities.