File size: 4,554 Bytes
f365f9c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 |
# 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:
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.
|