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This project focuses on gathering a comprehensive dataset of genuine and spoofed images, which is then curated and split to train a YOLOv8 model. The goal is to develop a robust system capable of accurately detecting and preventing spoofing attempts.

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Sookeyy-12/AntiSpoofing

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AntiSpoofing - Datacollection and Training

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Project Overview

The AntiSpoofing project aims to develop a system for detecting and preventing spoofing attacks. This README provides an overview of the project, including its

  • Purpose
  • Features

NOTE: To run the project locally, follow the instructions in the RunLocally.md file.

Purpose

The purpose of this AntiSpoofing project is to enhance the security of a system by detecting and preventing spoofing attacks. Spoofing attacks involve impersonating a legitimate user or device to gain unauthorized access or deceive the system. By implementing effective anti-spoofing measures, the project aims to mitigate the risks associated with such attacks.

In addition, the project also supports the use of custom data for training the anti-spoofing system. This allows users to incorporate their own data sets, specific to their application or environment, to improve the accuracy and effectiveness of the system.
The project provides guidelines and tools for preprocessing and integrating custom data into the training process.

Features

The AntiSpoofing project offers the following features:

  • Data Collection: The project provides tools and guidelines for collecting relevant data to train the anti-spoofing system. This includes capturing various types of spoofing attempts and genuine user interactions.

  • Training: The project includes training modules that utilize the collected data to train YOLOv8. These models will be used for detecting and classifying spoofing attempts.

  • Evaluation: The project provides evaluation metrics and techniques to assess the performance of the trained models. This helps in fine-tuning the system and improving its accuracy.

  • Integration: The project aims to provide integration capabilities with existing systems or frameworks. This allows the anti-spoofing system to be seamlessly incorporated into different applications or platforms.

Contributing

Contributions to the AntiSpoofing project are welcome. If you would like to contribute, please follow the guidelines outlined in the project's CONTRIBUTING.md file (yet to be made).

License

The AntiSpoofing project is licensed under the MIT License. For more details, see the LICENSE file.

Contact

For any questions or inquiries, please feel free to reach me out (github@Sookeyy12)

About

This project focuses on gathering a comprehensive dataset of genuine and spoofed images, which is then curated and split to train a YOLOv8 model. The goal is to develop a robust system capable of accurately detecting and preventing spoofing attempts.

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