<i>In situ</i> cryptography in a neuromorphic vision sensor based on light-driven memristors

TLDR

Vision sensors collect vast sensitive data, yet existing cryptosystems demand computational resources that exceed the limited processing capabilities of such devices. We propose and experimentally demonstrate an in‑situ image cryptography scheme using a neuromorphic vision sensor with all‑optically controlled memristors. Leveraging the light‑wavelength and irradiation‑history‑dependent bidirectional persistent photoconductivity of the memristors, the sensor can store, encrypt, decrypt, denoise, and destroy images, and the decrypted image can be encoded and accurately recognized by a memristive neural network. The encrypted and destroyed images resist hacking even by trained neural networks, enabling complete cryptographic operations on the sensor and providing a simple, efficient solution to vision‑sensor security challenges.

Abstract

Vision sensors are becoming increasingly ubiquitous, and they continuously collect, store, communicate, and process vast amount of sensitive data that are vulnerable to being stolen and misused. Existing cryptosystems based on complex cipher algorithms generally require extensive computational resources, making them difficult to use in vision sensors that have limited processing capabilities. Here, we propose and experimentally demonstrate a novel in situ image cryptography scheme based on a neuromorphic vision sensor comprising all-optically controlled (AOC) memristors. Due to the unique light wavelength and irradiation history-dependent bidirectional persistent photoconductivity of AOC memristors, a visual image can be stored, encrypted, decrypted, denoised, and destroyed within a vision sensor. A decrypted image can be encoded in situ and then accurately recognized through a memristive neural network. Encrypted and destroyed images are capable of withstanding hacking attacks even with trained neural networks. Our cryptography scheme enables complete cryptographic operations entirely on a sensor and, therefore, effectively safeguards visual information. This work provides a simple yet efficient solution to the security challenges faced by vision sensors.