CyberSuraksha: A Multimodal Deepfake Detection Framework

Abstract

In the era of artificial intelligence, the creation of realistic yet synthetic media — known as deepfakes — poses significant threats to personal privacy, financial security, and social trust. Cyber Surakshaisan AI-driven framework designed to detect manipulated digital content across multiple modalities including image, audio, and video, while simultaneously educating users about cyber hygiene and online safety. The system leverages Google’s Gemini API for multimodal analysis, presents an interactive user interface, and integrates explainable AI techniques to provide real-time detection, interpretability, and actionable insights. This paper discusses the architecture, methodology, features, and potential applications of CyberSuraksha, demonstrating a practical approach to bridging AI research and user-centric cybersecurity solutions.

Introduction

Deepfake technology has advanced rapidly, enabling realistic synthetic media but posing risks like misinformation, identity theft, and harassment. Detecting deepfakes is challenging due to diverse formats and subtle manipulations. CyberSuraksha addresses this by combining AI-powered detection, explainability, and user education in a single platform.

Key Points:

• Literature Review: Traditional detection methods (CNNs, LSTMs, ResNets) struggle with generalization, multimodal analysis, and transparency. Recent trends highlight the need for explainable AI and digital literacy tools. CyberSuraksha uniquely integrates detection with user education.

• Problem Statement: Existing solutions are limited to single media types and backend detection, lacking interpretability and educational value. CyberSuraksha provides multimodal detection, transparent results, and digital safety guidance.

• Methodology:

  • Architecture: Frontend-driven system with a responsive interface; media uploads analyzed via Google Gemini API.

  • AI Integration: Initial custom LSTM and ResNet models were replaced by Gemini API for reliable multimodal analysis.

  • Detection Process: Media converted to Base64, analyzed via API, and results visualized with overlays, charts, and explanations.

  • User Education: Stepwise anomaly explanations, audio guides, and interactive quizzes enhance digital literacy.

  • Multimodal Handling: Images, videos, and audio analyzed for anomalies across texture, motion, pitch, and synthetic patterns.

  • Frontend & UX: Built with React, TypeScript, Tailwind CSS, Web Audio API, and SVG animations for a polished, responsive interface.

• CyberSuraksha AI Chatbot: Provides interactive guidance, explains detection results, and maintains persistent context for personalized user support.

• Results: Reliable multimodal detection, improved interpretability, user trust, and accessibility for non-technical users. Lays groundwork for real-time and institutional applications.

• Advantages: Combines accuracy, transparency, educational components, and frontend-driven scalability.

• Limitations & Future Work: Prototype lacks backend and real-time video support; plans include backend integration, live-stream detection, CI/CD pipeline, and organizational dashboards.

Conclusion

CyberSuraksha provides a practical, user-centered solution to the increasing threat of deepfakes. By combining multimodal AI capabilities with interpretability and cybersecurity education, it empowers users to identify manipulated content while developing safer online practices. The system presents a scalable framework for future research and real-world deployment, highlighting the significance of multimodal AI in digital safety.

References

[1] R. Tolosana, R. Vera-Rodriguez, J. Fierrez, A. Morales, and J. Ortega-Garcia, “DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection,” Information Fusion, vol. 64, pp. 131–148, 2020. [2] A. Rossler et al., “FaceForensics++: Learning to Detect Manipulated Facial Images,” IEEE International Conference on Computer Vision, 2019. [3] Google AI, “Gemini API Documentation,” 2025.

Copyright

Copyright © 2025 Chakrapani D S, Akshata T Rathod , Aqsah Sehreen, Dhanalakshmi S, Inchara Poovaiah A . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.