Device Light Fingerprints Identification Using MCU-Based Deep Learning Approach

We introduce device identification using the light fingerprint by a MCU-based deep learning approach. At first, we observe that minor differences exist for individual components of lighting equipment. The corresponding difference produces a unique phenomenon in the frequency spectrum. Therefore, we adopt deep learning approaches for developing a mobile phone light fingerprint identification system and implementing it on a low-cost microcontroller platform. The screen light of the mobile phone is analyzed to obtain the features of unique light fingerprints. We utilize the convolutional neural network, the improved multi-class greedy autoencoder and variational autoencoder with domain adaptation techniques to develop the identification algorithm. Finally, the Bayesian optimization technique is used to optimize the hyper-parameters of models for implementing in the microprocessor. The corresponding comparisons are introduced to demonstrate the performance. The multi-class greedy autoencoder algorithm produces results with an overall accuracy rate and abnormal sample detection rate of 99.67% and 99.85%, respectively. Only a single model needs to be added or deleted for updating new authentication data and this does not affect the identification ability of all models. This results in greater flexibility in real-life applications and potential for expansion to other fields, such as smart buildings and automated robots.