Fake Image Detection using Multimodal Techniques

This project aims to detect fake images by leveraging multimodal techniques, which include analyzing the image content, associated text (captions or descriptions), and metadata (EXIF data). The system integrates deep learning and machine learning models to make an accurate and robust prediction.

Purpose

The main objectives behind developing this Fake Image Detection system are as follows:

1. Enhance Detection Accuracy: By integrating multiple data modalities, the system aims to improve the accuracy of detecting fake images compared to using a single modality.

2. Leverage Diverse Data: Utilize image content, textual information, and metadata to provide a comprehensive analysis of image authenticity.

3. Promote Robustness: Combining different types of data makes the detection system more robust against various types of forgeries and manipulations.

4. Track Performance: Allow for monitoring and evaluation of model performance to continually improve detection accuracy.
   

Key Features

• Image Analysis: Uses a Convolutional Neural Network (CNN) to analyze the visual content of images.

• Text Analysis: Employs a Logistic Regression model to evaluate the textual descriptions or captions associated with the images.

• Metadata Analysis: Utilizes a Random Forest classifier to analyze the metadata (EXIF data) of images.

• Model Combination: Combines the outputs of individual models using a voting classifier to make a final decision on image authenticity.

• Evaluation Metrics: Provides performance metrics such as accuracy, precision, recall, and F1 score to evaluate the effectiveness of the detection system.
  

Technical Details

• Programming Language: Python
• Image Processing: OpenCV, TensorFlow/Keras
• Text Processing: NLTK, Scikit-learn
• Metadata Handling: Piexif
• Model Combination: Voting Classifier from Scikit-learn
  

Additional Resources

To enhance the functionality of the Fake Image Detection system, consider importing the following resources:

• Image Datasets: Collect real and fake images for training and evaluation.
• Text Datasets: Prepare textual data related to images for text analysis.
• Metadata: Extract metadata from images to be used in the metadata analysis.

How to Use

1. Clone the Repository:

   git clone https://github.com/yourusername/fake_image_detection.git
   

2. Navigate to the Project Directory:

   cd fake_image_detection
   

3. Install Dependencies:

   pip install -r requirements.txt
   

4. Run the Main Script:

   python main.py
   

   
   

Contributing

If you'd like to contribute to the Fake Image Detection project, please follow these steps:

1. Fork the repository.
2. Create a new branch: git checkout -b feature/new-feature.
3. Make your changes and commit them: git commit -m "Add new feature".
4. Push to the branch: git push origin feature/new-feature.
5. Create a pull request.

This README.md provides an overview of the project, setup instructions, file descriptions, and steps to run the project. It should help users understand and implement the fake image detection system effectively.