Deepfake Image and Forged Signature Detection using Machine Learning
Kusumieta Magoo, Tamim Choudhary Amity, Rashmi Gandhi, Madhav Sharma · 2025
The escalating sophistication of digital manipulation technologies poses unprecedented challenges to cybersecurity and digital content integrity. This research introduces an innovative machine learning detection model designed to combat deepfake images and forged signatures through advanced neural network architectures. Leveraging Convolutional Neural Networks (CNN) and sophisticated deep learning techniques, the proposed model develops a comprehensive framework for identifying manipulated digital content. By utilizing a diverse dataset of authentic and fabricated images, the research creates a robust detection mechanism capable of extracting intricate visual features with remarkable precision. Experimental validation demonstrates the model’s exceptional performance, significantly surpassing conventional detection methodologies. The approach achieves superior accuracy in distinguishing between genuine and manipulated content, showcasing remarkable adaptability across various digital forgery techniques. The research contributes critically to cybersecurity by providing a sophisticated mechanism for digital content authentication. Beyond technological innovation, the study addresses the urgent need for reliable detection strategies in an increasingly complex digital landscape. Future investigations will focus on expanding the model’s capabilities and exploring broader applications in digital forensics and security verification.