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🎬 VideoDeepFakeDetection uses AI to authenticate videos through a multi-step process, identifying potential deepfakes for enhanced content reliability.

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VideoDeepFakeDetection

Application that detects the originality of video files with artificial intelligence.

Setup Environment

# Make sure your PIP is up to date
pip install -U pip wheel setuptools

# Install required dependencies
pip install -r requirements.txt

Application

1

  • Load your video(.mp4) file and test whether the file is real or not.

2

Overview

1- The video file is opened, and various video properties such as fps, width, and height are obtained.

2- Face detection is performed using MTCNN (Multi-Task Cascaded Convolutional Networks).

3- The detected face is transformed into a feature vector using a pre-trained Inception Resnet V1 model (InceptionResnetV1).

4- A comparison is made with the face in the previous frame, and a similarity score is calculated.

5- Similarity scores below a certain threshold are considered as indicative of a deepfake.

6- If deepfakes are detected in a consecutive number of frames, it is marked as a deepfake, and a frame is added to the video.

7- Processed frames are written to an output video file.

Contributing

If you want to contribute to this project, please follow these steps:

  • Fork: Fork this repository to your GitHub account.
  • Create a Branch: Create a new branch to add a new feature or fix a bug.
  • Commit: Add clear commit messages explaining your changes.
  • Push: Push your changes to the repository you forked.
  • Pull Request: Create a pull request on GitHub.

License

Our project is licensed under the MIT License.

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🎬 VideoDeepFakeDetection uses AI to authenticate videos through a multi-step process, identifying potential deepfakes for enhanced content reliability.

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