Deepfake Detection System

Overview

This project addresses the growing threat of digital misinformation by developing a robust Deepfake Detection System. It utilizes advanced deep learning techniques to analyze video and audio content, identifying signs of manipulation that are invisible to the naked eye.

The system employs a multi-stage analysis pipeline that processes video frames, audio tracks, and temporal consistency simultaneously, producing a weighted confidence score that indicates the likelihood of manipulation. The goal is to provide media organizations and individuals with a reliable tool for verifying content authenticity.

Key Features

• Frame-by-frame video analysis using Convolutional Neural Networks (CNNs)
• Audio consistency checking to detect voice synthesis and splicing
• Temporal analysis for detecting inter-frame artifacts and unnatural motion
• Real-time detection capabilities for live streams and uploaded media
• Comprehensive report generation with per-frame confidence scores
• User-friendly web interface for media upload and batch analysis

Architecture & System Design

The detection engine is built around a three-stream architecture:

• Visual Stream: A fine-tuned EfficientNet backbone processes individual frames, detecting facial artifacts, inconsistent lighting, and compression anomalies that indicate manipulation
• Audio Stream: Mel-spectrogram analysis with a separate CNN identifies voice synthesis patterns, unnatural pitch shifts, and audio-visual sync mismatches
• Temporal Stream: An LSTM layer analyzes sequences of frame-level predictions to detect inter-frame inconsistencies and flickering artifacts

The backend API is built with FastAPI for high-throughput inference, while OpenCV handles efficient video decoding and frame extraction. The React-based frontend provides a clean interface for uploading media, viewing real-time analysis progress, and exploring detection results with per-frame heatmaps.

Challenges & Solutions

• Dataset Imbalance: Real-world deepfake datasets are heavily skewed. Addressed this with stratified sampling and focal loss to prevent the model from defaulting to the majority class
• Inference Speed: Full video analysis was initially too slow for practical use. Implemented keyframe extraction and adaptive sampling to reduce processing time by 60% while maintaining accuracy
• Generalization: Models trained on one deepfake method often fail on others. Used multi-dataset training with domain randomization to improve cross-method detection

Screenshots