Deep Learning-Based Framework For Multimedia Forgery Detection

This paper presented a Deep Learning-Based Multimedia Forgery Detection Framework for identifying manipulated images, videos, audio recordings, and AI-generated text using multimodal AI techniques. The proposed framework integrates CNNs, SVMs, KNN, Bayesian classifiers, and NLP methods to improve forgery detection accuracy and reliability across multiple media modalities. Experimental results demonstrated that the system achieved high detection accuracy while reducing false positives through multimodal decision fusion. The integration of React.js frontend architecture, FastAPI backend processing, and deep learning-based detection models provides a scalable and efficient solution for multimedia forgery detection. The proposed framework can be effectively applied in digital forensics, cybersecurity, social media verification, and online content authentication systems to reduce misinformation and improve digital media trustworthiness.

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