Commit History
-
Update code.py
Prathmesh PatilVerified signatureThis code is a Python script that demonstrates how to create a deep learning model for binary classification using the VGG16 architecture pre-trained on the ImageNet dataset. Here's a breakdown of what each part of the code does: Importing Libraries: The necessary libraries are imported, including NumPy, Keras, OpenCV (cv2), and TensorFlow. Load Pre-trained VGG16 Model: The VGG16 model is loaded with pre-trained weights from the ImageNet dataset. The include_top=False argument indicates that the fully connected layers (top layers) of the VGG16 model will not be included, allowing for customization with additional layers. Freeze Base Model Layers: All layers of the pre-trained VGG16 model are set to non-trainable (frozen) to prevent their weights from being updated during training. Add Custom Layers for Classification: Additional layers are added on top of the pre-trained VGG16 base model to adapt it for binary classification (in this case, face authentication). These layers include a …9341581 -
Create code.py
Prathmesh PatilVerified signaturea154b96 -
Update README.md
Prathmesh PatilVerified signaturefe0930d -
Update README.md
Prathmesh PatilVerified signatured715b47 -
kaggle datasets download -d sachchitkunichetty/rvf10k
Prathmesh PatilVerified signature6ecd176 -
FaceAuthenticator: Real Face Detection Model FaceAuthenticator is a deep learning model designed for accurate real face detection, developed to combat the growing threat of deepfake content. This model leverages the powerful VGG16 convolutional neural network architecture and advanced transfer learning techniques to reliably distinguish between authentic human faces and synthetic, AI-generated faces. Key Features Robust Face Detection: The FaceAuthenticator model delivers high-accuracy predictions, enabling reliable authentication of real human faces and effective detection of deepfake content. VGG16 Backbone: The model utilizes the pre-trained VGG16 architecture as its foundation, taking advantage of its strong performance in computer vision tasks. Transfer Learning: The model employs transfer learning techniques to adapt the VGG16 backbone for the specific task of real face detection, retaining the learned low-level visual features. Comprehensive Training: The model has been fine-tuned on a curated dataset of real and deepfake facial images to enhance its ability to accurately identify authentic faces. Python and Keras: The FaceAuthenticator model is implemented in Python using the Keras deep learning library, ensuring compatibility and ease of integration with a wide range of applications and platforms. Usage To use the FaceAuthenticator model, follow these steps: Install the required dependencies, including Python, Keras, and any necessary image processing libraries. Load the pre-trained FaceAuthenticator model using the provided Keras model file. Preprocess your input images according to the model's requirements. Pass the preprocessed images through the model to obtain the real face detection predictions. Integrate the model's predictions into your application or system to enhance its ability to combat deepfake content. Deployment The FaceAuthenticator model can be deployed in various environments, including local servers, cloud platforms, and edge devices. Refer to the provided deployment guides for specific instructions on how to set up and run the model in your desired environment. Contributions and Support We welcome contributions and feedback from the community to further improve the FaceAuthenticator model. If you encounter any issues or have suggestions, please feel free to open an issue or submit a pull request on the project's GitHub repository. For support and assistance, please contact the project maintainers or refer to the documentation.
Prathmesh PatilVerified signaturea0185cc -
Update README.md
Prathmesh PatilVerified signatureFaceAuthenticator: Real Face Detection Model FaceAuthenticator is a deep learning model designed for accurate real face detection, developed to combat the growing threat of deepfake content. This model leverages the powerful VGG16 convolutional neural network architecture and advanced transfer learning techniques to reliably distinguish between authentic human faces and synthetic, AI-generated faces. Key Features Robust Face Detection: The FaceAuthenticator model delivers high-accuracy predictions, enabling reliable authentication of real human faces and effective detection of deepfake content. VGG16 Backbone: The model utilizes the pre-trained VGG16 architecture as its foundation, taking advantage of its strong performance in computer vision tasks. Transfer Learning: The model employs transfer learning techniques to adapt the VGG16 backbone for the specific task of real face detection, retaining the learned low-level visual features. Comprehensive Training: The model has been fine-tun…6c02178 -
Upload fake_real_face_classification_model.keras
Prathmesh PatilVerified signatureFaceAuthenticator is a deep learning model designed for real face detection, specifically developed to combat deepfake content. This model utilizes the powerful VGG16 convolutional neural network architecture, which has been extensively trained on the ImageNet dataset to achieve state-of-the-art performance in image classification and recognition tasks. The FaceAuthenticator model has been fine-tuned and optimized for the task of distinguishing between real human faces and deepfake or synthetic faces. It leverages the robust feature extraction capabilities of the VGG16 backbone, combined with additional dense layers and fine-tuning on a curated dataset of real and deepfake facial images. Key features of the FaceAuthenticator model: Utilizes the pre-trained VGG16 model as the base, taking advantage of its strong performance in computer vision tasks. Employs transfer learning techniques to adapt the model for the specific task of real face detection, retaining the learned low-level visua…c35b69e -
initial commit
Prathmesh PatilVerified signature6d74f68