Threat actors use generative AI deepfakes to create or manipulate facial images for identity fraud. The process involves either generating new faces or altering existing images tailored to the victim's identity or created to pass as a legitimate user using generative AI. To address the problem specific image capture security protocols and a robust liveness algorithm should be used to effectively block generative AI spoofing attempts.

This guide outlines essential practices for capturing and uploading ID documents and selfies, ensuring a smooth and secure digital onboarding experience. By adhering to these guidelines, users can significantly enhance the accuracy of identity verification, reducing errors and improving the overall success rate.

As the landscape of digital identity verification rapidly evolves, Artificial Intelligence (AI) is at the forefront, reshaping traditional approaches. This deep dive into AI's role in identity verification is for those familiar with the nuances of data science and computer science.

Deep Learning, particularly Convolutional Neural Networks (CNNs), has revolutionized facial recognition technology. This layered approach enables the model to discern intricate facial patterns, enhancing recognition accuracy. Backpropagation, a key mechanism in CNNs, refines these features, significantly improving the model's ability to distinguish subtle facial characteristics.