INTEGRATED DEEPFAKE DETECTION AND SECURE DEPLOYMENT SYSTEM
Mrs.S.Nandhini1, Abinesh M2, Ancy Jemi Goldbell P3, Anishka J4
1Professor and Head of the Department of Computer Science & Engineering, Sri Shakthi Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India, Email: nandhiniscse@srishakthi.ac.in
2Student, Department of Computer Science & Engineering, Sri Shakthi Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India, Email: abineshm22cse@srishakthi.ac.in
3Student, Department of Computer Science & Engineering, Sri Shakthi Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India, Email: ancyjemigoldbellp22cse@srishakthi.ac.in
4Student, Department of Computer Science & Engineering, Sri Shakthi Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India, Email: anishkaj22cse@srishakthi.ac.in
ABSTRACT: The Deepfake Detection The Deepfake Detection System is an advanced web-based application designed to identify manipulated or AI-generated media using machine learning techniques. With the rapid evolution of deep learning, creating highly realistic fake images and videos has become easier, raising serious concerns about privacy, security, and digital authenticity. This system addresses these challenges by analyzing subtle visual patterns and inconsistencies that are difficult for humans to detect. It integrates a trained machine learning model that processes uploaded media, extracts meaningful features, and classifies content as real or fake. The platform ensures efficient communication between the user interface and the detection engine, delivering accurate and near real-time results.
In addition to media analysis, the system extends its functionality by incorporating a secure artifact analysis module that supports ZIP file processing. Users can upload compressed files containing source code or application components, which are automatically extracted and analyzed. The system performs dependency inspection, vulnerability detection, and version compatibility checks to identify outdated or insecure packages. Based on this analysis, it provides upgrade recommendations and generates a deployment-ready script that helps users securely update and run their applications.
Built with scalability, reliability, and performance in mind, the system enables both authenticity verification and software security validation within a unified platform. By leveraging advanced algorithms, including CNN-based architectures, along with automated security analysis techniques, the platform enhances trust in digital content while also ensuring the integrity and safety of software artifacts.
