Deepfake Detection using Deep learning

This project demonstrates that effective deepfake detection is achievable through the combination of sophisticated deep learning models and practical system design. While challenges remain, particularly in maintaining accuracy across diverse generation methods and improving generalization, the achieved results of 84% accuracy show significant promise for real-world deployment. The developed system represents a meaningful contribution to the field of digital forensics and cybersecurity. By providing an accessible web-based platform alongside a robust detection model, this project makes deepfake detection technology available to non-technical users while maintaining scientific rigor. The path forward involves continued research into more robust detection methods, expansion of the system's capabilities to include multi-modal analysis, and careful consideration of the ethical implications of this technology. As deepfake generation techniques continue to evolve, so must our detection capabilities, requiring ongoing collaboration between researchers, practitioners, and policymakers. Ultimately, while detection alone cannot solve the deepfake problem, it represents a critical component of a comprehensive strategy that includes technical solutions, media literacy, regulatory frameworks, and social awareness.

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