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| title: DeepFake Detector | |
| emoji: 🕵️♂️ | |
| colorFrom: red | |
| colorTo: purple | |
| sdk: gradio | |
| sdk_version: "5.35.0" | |
| app_file: app.py | |
| pinned: false | |
| # 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. | |