https://github.com/divith123/deepsecure-ai
DeepSecure-AI is an open-source deepfake detection tool leveraging advanced AI models like EfficientNetV2 and MTCNN for high-accuracy analysis of images, videos, and audios. With real-time frame-by-frame video processing and a user-friendly Gradio interface, it empowers researchers and developers to combat media falsification effectively.
https://github.com/divith123/deepsecure-ai
ai-models deepfake-detection machine-learning python
Last synced: 19 days ago
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DeepSecure-AI is an open-source deepfake detection tool leveraging advanced AI models like EfficientNetV2 and MTCNN for high-accuracy analysis of images, videos, and audios. With real-time frame-by-frame video processing and a user-friendly Gradio interface, it empowers researchers and developers to combat media falsification effectively.
- Host: GitHub
- URL: https://github.com/divith123/deepsecure-ai
- Owner: Divith123
- Created: 2024-11-19T04:18:56.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2024-12-31T08:26:30.000Z (4 months ago)
- Last Synced: 2025-04-09T23:50:31.735Z (19 days ago)
- Topics: ai-models, deepfake-detection, machine-learning, python
- Language: Python
- Homepage: https://huggingface.co/spaces/ninjaonsteroids/DeepSecure-AI
- Size: 1.42 MB
- Stars: 6
- Watchers: 1
- Forks: 3
- Open Issues: 2
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Metadata Files:
- Readme: README.md
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README
# 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...
---
## 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.
---
## 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](#)
---
## How It Works
DeepSecure-AI uses the following architecture:
1. Face Detection:
The [MTCNN](https://arxiv.org/abs/1604.02878) 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](https://arxiv.org/abs/2104.00298) 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.
---
## 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:
git clone https://github.com/Divith123/DeepSecure-AI.git
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.
---
## 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.
---
## 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.
---
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
---
## 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](CONTRIBUTING.md).
---
## References
- Li et al. (2020): [Celeb-DF(V2)](https://arxiv.org/abs/2008.06456)
- Rossler et al. (2019): [FaceForensics++](https://arxiv.org/abs/1901.08971)
- Timesler (2020): [Facial Recognition Model in PyTorch](https://www.kaggle.com/timesler/facial-recognition-model-in-pytorch)
---
### 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.