QD) layer, the device induces probabilistic trapping-de-trapping dynamics, producing random photospike currents under optical pulse trains. The spike currents show high entropy and enable multi-level random number generation beyond binary, providing ternary outputs with near-ideal statistics (33.30% uniformity, 33.28% inter-Hamming distance) and full success in all 15 NIST tests. We further develop an image authenticity verification system by integrating the PS-TRNG with a mobile platform and custom-designed circuit board, enabling hardware-based detection of unauthorized image modifications. The random numbers are embedded as a hidden layer within the image data without degrading visual quality, enabling detection of unauthorized modifications. The system can successfully identify image modifications, even those involving highly sophisticated manipulations generated by artificial intelligence (AI)-based image editing tools. The device maintains stable operation over 2 million cycles and remains reliable even after more than 460 days, demonstrating its long-term stability.

ion migration induce a phase transition, leading to sharp resistance switching and efficient spiking. This device successfully mimics key neuronal dynamics, including leaky integrate-and-fire, threshold tuning, and spatiotemporal dynamics, without requiring auxiliary reset circuits. Furthermore, SNN is constructed by integrating CIPS-based synaptic and neuron devices and evaluate face classification performance using an unsupervised learning approach, achieving a recognition accuracy of 95.83% via the lateral inhibition function of the neuron device. The findings highlight the potential of CIPS TS-FET as energy-efficient spiking neuron device applications for next-generation SNN-based neuromorphic computing systems.