Paper Information Recording and Security Protection Using Invisible Ink and Artificial Intelligence

Area of Science:

• Materials Science
• Artificial Intelligence
• Information Security

Background:

• Conventional paper security relies on predictable stimuli-responsive materials, which are vulnerable to cracking.
• Existing methods lack robust protection against sophisticated decryption techniques.

Purpose of the Study:

• To develop an advanced paper information protection scheme.
• To integrate fluorescent invisible ink with artificial intelligence for enhanced security.
• To create a system resistant to conventional cracking methods.

Main Methods:

• Prepared a fluorescent invisible ink using water-dispersed carbon nanoparticles with high quantum yield and stability.
• Developed a five-layer convolutional neural network (CNN) trained on ultraviolet (UV) light-excited symbols from the invisible ink.
• Implemented a security scheme where information is only readable with the trained CNN and UV irradiation.

Main Results:

• The developed invisible ink demonstrated high quantum yield and excellent light and salt stability.
• The trained CNN successfully decrypted information exclusively under UV light, rendering misleading messages without the trained model or UV.
• The scheme allows for the creation of complex, unpredictable password books for advanced security.

Conclusions:

• The proposed strategy offers a new paradigm for high-level paper information encryption.
• This work highlights novel applications for carbon nanoparticles and artificial intelligence in security.
• The combination of invisible ink, AI, and UV light provides a robust and sophisticated information protection system.