AI Interdisciplinary Authenticator

Problem Statement:

In the contemporary landscape of burgeoning artificial intelligence (AI) technologies, there exists an urgent need for a sophisticated authentication system capable of shielding users from potential exploitation by Generative AI or any AI/ML models generating content, encompassing text, images, audio, video, and beyond. This project, titled "AI Interdisciplinary Authenticator," addresses the critical issue of safeguarding user data, images, names, and other sensitive information from unauthorized use in AI Generative Algorithms.

Objectives:

User Registration and Protection: Implement a robust registration process empowering users to securely input and authenticate their personal information, including but not limited to name, address, contact details, videos, and images, thereby fortifying protection against AI-generated content.

  • Authentication Mechanism: Develop an advanced authentication mechanism that operates seamlessly to thwart unauthorized access to user data, ensuring a stringent defense against potential AI exploits.

Expected Outcomes:

Users leveraging the "AI Interdisciplinary Authenticator" for authentication purposes will experience a heightened level of security. Consequently, any AI model interfacing with this authenticator will exhibit a conscientious disregard for prompts associated with users who have proactively registered to shield their information.

Implementation Strategies:

Secure User Registration: Employ state-of-the-art security measures in the registration process, ensuring the confidentiality and integrity of user-provided data.

  • AI Model Integration: Establish a robust API or integration framework allowing AI models to seamlessly verify a user's registration status and subsequently tailor their behavior accordingly.

  • Encrypted Storage: Institute secure data storage protocols, utilizing encryption methodologies and industry best practices to fortify the integrity of stored user information.

  • Intelligent Prompt Filtering: Develop sophisticated algorithms within AI models to intelligently recognize and abstain from generating content related to users who have proactively registered for enhanced protection.

Challenges and Considerations:

  • Privacy Paradigm: Strike a delicate balance between heightened security measures and user privacy, ensuring adherence to stringent privacy standards to engender user trust.

  • Adaptability and Future-Proofing: Design the system with an innate flexibility to adapt to the ever-evolving landscape of AI technologies, safeguarding against potential modification of exploitation techniques.

  • Accuracy Assurance: Institute rigorous testing and validation processes to guarantee the accuracy and efficacy of the authentication system in identifying and protecting users against AI-generated content.

The "AI Interdisciplinary Authenticator" project aligns seamlessly with contemporary concerns surrounding data privacy and the responsible deployment of AI technologies. By amalgamating cutting-edge user authentication techniques with AI model integration, the project aspires to contribute to a heightened level of security and resilience in the face of evolving threats.

Dependencies

  • llama2-uncensored: [version]

    • Description: Llama 2 Uncensored is based on Meta’s Llama 2 model, and was created by George Sung and Jarrad Hope using the process defined by Eric Hartford
    • Installation: https://ollama.com
  • customtkinter: [version]

    • Description: Customtkinter is a customized version of the Tkinter library, offering additional features and functionalities for building graphical user interfaces (GUIs) in Python.
    • Installation: pip install customtkinter
  • Python: 3.12

    • Description: Python programming language, widely used for various applications including web development, data analysis, artificial intelligence, and more.
    • Installation: Refer to Python's official website for installation instructions.
  • Linux: (Any)

    • Description: Linux operating system, known for its stability, security, and open-source nature, commonly used for server hosting and development environments.
    • Installation: Refer to Linux's official website for installation instructions.
  • SQLite:

    • Description: SQLite is a lightweight, self-contained, serverless SQL database engine, commonly used for embedded systems, mobile applications, and small-scale database management.
    • Installation: SQLite is included in Python's standard library. No additional installation needed.

Requirements

  • Python (>= 3.10)
  • Linux operating system (recommended)
  • SQLite (included with Python)
  • Large Language Model: llama2-uncensored

Installation

  1. Clone the repository:

    git clone https://github.com/QuantumByteStudios/AI-Interdisciplinary-Authenticator
  2. Install Python (if not already installed). Refer to Python's official website for installation instructions.

  3. Install project dependencies:

    pip install -r requirements.txt

License

LICENSE