Abstract
A new era of hyper-realistic synthetic media has been brought about by the quick development of deep-fake technology, which is powered by artificial intelligence and deep learning. This presents serious problems for online information reliability, personal privacy, and digital security. These media manipulations use speech, motion, and facial emotions to create incredibly realistic photos, movies, and audio that can be used for fraud, disinformation, and public perception modification. Using specialized deep learning architectures, this project seeks to address these issues by creating a comprehensive, multi- modal deepfake detection framework. This includes a hybrid CNN and Long Short-Term Memory (LSTM) model for capturing temporal inconsistencies in video frames, Convolutional Neural Networks (CNN) for detecting spatial artifacts in images, and an Artificial Neural Network (ANN) trained on spectral features and Mel Frequency Cepstral Coefficients (MFCCs) is used to differentiate between synthetic and real audio.
The models were trained on curated datasets from credible open-source repositories, each with thousands of annotated real and fake samples to ensure robust generalization. These trained models were effortlessly incorporated into an interactive online application built with Streamlit, allowing users to input media files and instantly receive modality-specific authenticity estimates. The algorithm consistently achieved high accuracy, precision, and recall across all media types. This project combines advanced deep learning with a simple interface to offer a scalable deepfake detection solution.