Cybersecurity · Big Data Analytics · Deep Learning

Deepfake Detection System

An advanced AI-powered system that detects manipulated videos and audio using Xception-based neural networks, frame-wise analysis, and big data analytics techniques — helping safeguard digital trust in cybersecurity environments.

Python / Django Xception CNN TensorFlow / Keras FFmpeg Big Data Analytics JavaScript

View Repository How It Works

Landing Page
Advanced DeepFake Detection System — 99.7% accuracy, <2s processing, 24/7 availability

Frame Analysis Preview
15 frames analyzed — suspicious frames highlighted in red, clean frames in green with confidence %

URL Analysis
Analyze any YouTube, Vimeo, or direct video URL — no download required

Results Dashboard
Overall distribution donut chart + frame-by-frame probability analysis graph

Performance Metrics

Key Performance Indicators

🎯

99.7%

Detection Accuracy

⚡

<2s

Processing Time

🖼️

15+

Frames Per Analysis

🧠

Xception

Neural Network Model

🎵

Video + Audio

Media Types Supported

🌐

URL + Upload

Input Methods

Detection Pipeline

How Detection Works

A multi-stage pipeline from media input to final confidence score report.

01📤Video Upload / URL

02🎬FFmpeg Frame Extraction

03🧠Xception Inference

04📊Frame-wise Scoring

05📈Analytics Charts

06🚨Verdict Report

Overview

Problem & Solution

🔴 The Cybersecurity Challenge

Deepfake videos are increasingly used in fraud, impersonation, and disinformation
Manual review of video content is time-consuming and unreliable
Existing tools lack frame-level granularity and audio analysis
No accessible web interface for non-technical security analysts

✅ How This System Solves It

Xception neural network pre-trained on deepfake datasets
FFmpeg extracts and analyzes every frame independently
Visual confidence scores per frame with color-coded alerts
Supports YouTube, Vimeo URLs and direct file uploads
Big data analytics for processing large-scale media

Capabilities

Core Features

🎬

Video Analysis

Upload video files or provide YouTube/Vimeo/direct URLs. FFmpeg extracts frames at defined intervals for comprehensive analysis.

🎵

Audio Analysis

Separate audio track extraction and analysis to detect audio manipulation independent of video frames.

🖼️

Frame-wise Analysis

Each frame receives an individual real/fake probability score. Suspicious frames are highlighted with their confidence percentage.

📊

Visual Analytics

Interactive donut chart for overall distribution and a line graph showing fake vs. real probability across all analyzed frames.

🧠

Xception Model

Leverages the Xception depthwise separable convolution architecture — proven effective on FaceForensics++ deepfake datasets.

🌐

Web Interface

Clean, accessible Django-powered web UI allowing analysts to submit and review results without any command-line expertise.

Architecture

Technology Stack

Python 3.8+

Django 3.x

TensorFlow / Keras

FFmpeg

Xception CNN

SQLite

Chart.js

Big Data Analytics

FAQ

Frequently Asked Questions

The system uses Xception-based neural networks — an architecture developed by Google that uses depthwise separable convolutions. It has been trained and validated on deepfake benchmark datasets (FaceForensics++). The model classifies each extracted video frame as real or fake with a confidence probability score.

Yes. The "Analyze URL" feature uses yt-dlp to download the video from YouTube, Vimeo, or direct video URLs. The system then processes the downloaded video through the same FFmpeg extraction and Xception inference pipeline as uploaded files.

FFmpeg extracts frames from the video at set intervals. Each frame is individually passed through the Xception model which returns a "fake probability" and "real probability" score. These per-frame scores are visualized as a line chart, and frames flagged as suspicious (fake probability > threshold) are highlighted in the frame preview grid with their confidence percentage.

The big data analytics layer enables processing of large-scale media datasets beyond single video analysis. It provides aggregated statistics, batch processing capabilities, and analytics infrastructure to handle high volumes of media for enterprise cybersecurity use cases — going beyond simple one-off video checks.

The GitHub repository contains the core Django project structure, requirements, and reference implementation. The pre-trained Xception model weights (dl_models/) and any sensitive configurations are not included in the public repository. The project is shared for educational and portfolio demonstration purposes.