Machine Learning–Enhanced Fluorescence Cryptography with <scp>NADH</scp> ‐Responsive Polymer Dots

The development of high security anticounterfeiting technologies is crucial to address the escalating demand for information security. Stimuli‐responsive fluorescent materials have garnered significant interest for advanced information encryption due to their dynamic signal outputs and compatibility with intelligent analysis tools. In this work, we fabricate and characterize semiconducting polymer dots (Pdots) that exhibit distinct, reproducible fluorescence signal changes in response to varying concentrations of NADH, a biologically relevant reducing agent. We establish a robust encryption‐decryption platform by integrating machine learning algorithms, wherein encrypted messages are encoded via NADH‐triggered fluorescence changes and decoded exclusively using pretrained models. This approach provides a secure, cost‐effective, and intelligent anticounterfeiting strategy, demonstrating the considerable potential of synergistically coupling chemical sensing with artificial intelligence for data protection.