MultimodalDeepfakeDetector

DeepSecure-AI

DeepSecure-AI is a powerful open-source tool designed to detect fake images, videos, and audios. Utilizing state-of-the-art deep learning techniques like EfficientNetV2 and MTCNN, DeepSecure-AI offers frame-by-frame video analysis, enabling high-accuracy deepfake detection. It's developed with a focus on ease of use, making it accessible for researchers, developers, and security analysts...

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Features

• Multimedia Detection: Detect deepfakes in images, videos, and audio files using a unified platform.
• High Accuracy: Leverages EfficientNetV2 for enhanced prediction performance and accurate results.
• Real-Time Video Analysis: Frame-by-frame analysis of videos with automatic face detection.
• User-Friendly Interface: Easy-to-use interface built with Gradio for uploading and processing media files.
• Open Source: Completely open source under the MIT license, making it available for developers to extend and improve.

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Demo-Data

You can test the deepfake detection capabilities of DeepSecure-AI by uploading your video files. The tool will analyze each frame of the video, detect faces, and determine the likelihood of the video being real or fake.

Examples:

1. Video1-fake-1-ff.mp4
2. Video6-real-1-ff.mp4

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How It Works

DeepSecure-AI uses the following architecture:

1. Face Detection:
   The MTCNN model detects faces in each frame of the video. If no face is detected, it will use the previous frame's face to ensure accuracy.

2. Fake vs. Real Classification:
   Once the face is detected, it's resized and fed into the EfficientNetV2 deep learning model, which determines the likelihood of the frame being real or fake.

3. Fake Confidence:
   A final prediction is generated as a percentage score, indicating the confidence that the media is fake.

4. Results:
   DeepSecure-AI provides an output video, highlighting the detected faces and a summary of whether the input is classified as real or fake.

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Project Setup

Prerequisites

Ensure you have the following installed:

• Python 3.10
• Gradio (pip install gradio)
• TensorFlow (pip install tensorflow)
• OpenCV (pip install opencv-python)
• PyTorch (pip install torch torchvision torchaudio)
• facenet-pytorch (pip install facenet-pytorch)
• MoviePy (pip install moviepy)

Installation

1. Clone the repository:

   cd DeepSecure-AI

2. Install required dependencies: pip install -r requirements.txt

3. Download the pre-trained model weights for EfficientNetV2 and place them in the project folder.

Running the Application

1. Launch the Gradio interface: python app.py

2. The web interface will be available locally. You can upload a video, and DeepSecure-AI will analyze and display results.

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Example Usage

Upload a video or image to DeepSecure-AI to detect fake media. Here are some sample predictions:

• Video Analysis: The tool will detect faces from each frame and classify whether the video is fake or real.
• Result Output: A GIF or MP4 file with the sequence of detected faces and classification result will be provided.

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Technologies Used

• TensorFlow: For building and training deep learning models.
• EfficientNetV2: The core model for image and video classification.
• MTCNN: For face detection in images and videos.
• OpenCV: For video processing and frame manipulation.
• MoviePy: For video editing and result generation.
• Gradio: To create a user-friendly interface for interacting with the deepfake detector.

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License

This project is licensed under the MIT License - see the LICENSE file for details.

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Contributions

Contributions are welcome! If you'd like to improve the tool, feel free to submit a pull request or raise an issue.

For more information, check the Contribution Guidelines.

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References

• Li et al. (2020): Celeb-DF(V2)
• Rossler et al. (2019): FaceForensics++
• Timesler (2020): Facial Recognition Model in PyTorch

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Disclaimer

DeepSecure-AI is a research project and is designed for educational purposes.Please use responsibly and always give proper credit when utilizing the model in your work.