Gen AI-Powered Signature Identifier and Image Manipulation
Carolin Gifia S,
IV year CSE, SNS College of Engineering, Coimbatore. Email: cccarolingifia@gmail.com
Indhumathi S,
IV year CSE, SNS College of Engineering, Coimbatore. Email: indhusenniyappan@gmail.com
Shivani R,
IV year CSE, SNS College of Engineering, Coimbatore. Email: pooranishivani682@gmail.com
Surya B,
IV year CSE, SNS College of Engineering, Coimbatore. Email: surya.baskaran.bs@gmail.com
Yogadharani M,
AP/CSE, SNS College of Engineering, Coimbatore. Email: yogadharanimcse@gmail.com
Abstract
The rise of digital transactions has significantly increased risks related to fraudulent digital signatures and identity impersonation across financial, legal, corporate, and online services. Traditional verification methods are often manual, time-consuming, and prone to inaccuracies, making them vulnerable to sophisticated forgeries. AI-driven solutions leveraging Deep Learning and Generative Adversarial Networks (GANs) provide a transformative approach to automating digital signature verification and identity authentication. These models compare a user’s uploaded image with their registered reference image, ensuring secure and accurate authentication.
By integrating real-time fraud detection mechanisms, this approach enhances security, minimizes identity fraud, and streamlines verification processes. Advanced AI algorithms continuously learn from data patterns to improve authentication accuracy and detect anomalies effectively. This system strengthens security frameworks across various sectors, including banking, corporate security, legal documentation, and digital services, ensuring trust and compliance. Additionally, this application introduces a generative AI-based assistant to address user queries related to identity verification and fraud prevention.
Keywords: AI, integrated care systems, primary healthcare, behavioral health services, and social determinants of health (SDOH) to provide comprehensive, patient-centric care, medical assistant.
