The paper suggests a technique that uses convolutional neural network (CNN) to identify fraudulent facial manipulation. The proposed method comprises teaching an intricate neural network using a comprehensive compilation of genuine and fake facial images. The structure of CNN includes several layers of convolution and pooling, which enable it to identify distinguishing features in the input images. Following its training, the model is employed to differentiate a presented facial image into either authentic or fraudulent. To determine the efficacy of the proposed technique, a standardized data set for identifying counterfeit or altered facial attributes was used. The suggested method presents various benefits compared to current techniques for detecting face spoofing. To start with, utilizing a deep CNN empowers the model to acquire complex and discerning characteristics from the input images, thus augmenting the precision of the categorization mission. Additionally, the suggested method is effective in terms of computational requirements, enabling its utilization in real-time scenarios. The proposed methodology is able to withstand a range of fraudulent tactics used on facial recognition systems, such as print and replay attacks. The findings from this study aid in the progression of face recognition technology by enhancing the accuracy and dependability of fraud detection systems. These improved systems have practical applications in security measures, biometric identification, and digital criminal investigations. The suggested method could substantially enhance the dependability and safety of facial recognition systems, consequently boosting their functional value and credibility (pp.40-46).
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