2026-04-17T02:32:23Zhttps://recercat.cat/oai/request

oai:recercat.cat:2445/1188382025-12-05T14:47:13Zcom_2072_1057col_2072_478821col_2072_478917

00925njm 22002777a 4500 dc Carnicer González, Arturo author Javidi, Bahram author 2017-12-20T16:21:15Z 2017-12-20T16:21:15Z 2017-05-10 2017-12-20T16:21:15Z Image information encoding using random phase masks produce speckle-like noise distributions when the sample is propagated in the Fresnel domain. As a result, information cannot be accessed by simple visual inspection. Phase masks can be easily implemented in practice by attaching cello-tape to the plain-text message. Conventional 2D-phase masks can be generalized to 3D by combining glass and diffusers resulting in a more complex, physical unclonable function. In this communication, we model the behavior of a 3D phase mask using a simple approach: light is propagated trough glass using the angular spectrum of plane waves whereas the diffusor is described as a random phase mask and a blurring effect on the amplitude of the propagated wave. Using different designs for the 3D phase mask and multiple samples, we demonstrate that classification is possible using the k-nearest neighbors and random forests machine learning algorithms. Seguretat informàtica Visualització tridimensional Xifratge (Informàtica) Processament òptic de dades Computer security Three-dimensional display systems Data encryption (Computer science) Optical data processing Validation of optical codes based on 3D nanostructures