Unified Framework for Deepfake Detection in Images, Videos, and Audio

Abstract

Unified Framework for Deepfake Detection in Images, Videos, and Audio is a comprehensive system designed to identify manipulated multimedia content across multiple modalities. The project utilizes state-of-the-art deep learning techniques such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and spectrogram-based analysis to detect synthetic media generated by advanced AI tools. By integrating visual and auditory feature extraction pipelines, the framework ensures robust and reliable identification of fake images, video frame manipulations, and voice synthesis-based deepfakes. The proposed unified approach eliminates the need for separate detection systems by combining multimodal data analysis within a single architecture. Developed using Python, TensorFlow, Flask, and React.js, the framework supports real-time detection, visual analytics, and alert mechanisms for suspected deepfake content. Experimental results demonstrate high detection accuracy and adaptability against emerging deepfake generation techniques, confirming the system’s potential in digital forensics, social media verification, and cybersecurity applications. This work emphasizes the importance of developing unified, AI-driven tools to combat misinformation and safeguard the authenticity of digital content in modern communication networks.