Image authentication method based on Fourier zero-frequency replacement and single-pixel self-calibration imaging by diffractive deep neural network

Abstract

The diffractive deep neural network is a novel network model that applies the principles of diffraction to neural networks, enabling machine learning tasks to be performed through optical principles. In this paper, a fully optical authentication model is developed using the diffractive deep neural network. The model utilizes terahertz light for propagation and combines it with a self-calibration single-pixel imaging model to construct a comprehensive optical authentication system with faster authentication speed. The proposed system filters the authentication images, establishes an optical connection with the Fourier zero-frequency response of the illumination pattern, and introduces the signal-to-noise ratio as a criterion for batch image authentication. Computer simulations demonstrate the fast speed and strong automation performance of the proposed optical authentication system, suggesting broad prospects for the combined application of diffractive deep neural networks and optical systems.