Abstract

DeepFake technology uses deep learning techniques to generate realistic fake images and videos, which can lead to misinformation and security threats. This project proposes a deep learning-based detection system using ResNet and Convolutional Neural Network (CNN) architectures to classify images and videos as real or fake. The system extracts facial features, processes them through trained neural networks, and predicts authenticity with high accuracy. This solution helps in enhancing digital media security and preventing misuse of AI-generated content.

Problem Statement

With the rapid growth of DeepFake technology, it has become difficult to distinguish between real and manipulated media. There is a need for an automated and accurate system that can detect DeepFake content effectively.

Existing System

Currently:

1. Manual verification of media content
2. Basic image forensic tools
3. Traditional machine learning models with low accuracy

Disadvantages

1. Low detection accuracy
2. Time-consuming process
3. Cannot handle complex DeepFakes
4. Not scalable for large datasets

Proposed System

The proposed system uses:

1. ResNet architecture for deep feature extraction
2. CNN for classification
3. Image preprocessing and face extraction
4. Binary classification (Real / Fake)

Advantages

1. High detection accuracy
2. Automated process
3. Scalable for large datasets
4. Efficient deep feature learning
5. Can be extended to video detection

System Requirements

Hardware Requirements

1. Processor: Intel i5 or above
2. RAM: 8GB minimum (16GB recommended)
3. GPU: NVIDIA GPU (for training)
4. Storage: 50GB

Software Requirements

1. Python
2. TensorFlow / PyTorch
3. OpenCV
4. NumPy, Pandas
5. Jupyter Notebook / VS Code
6. OS: Windows / Linux