# name
Validation of optical codes based on 3D nanostructures

# metadata.dc.description.abstract[].value
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.

# metadata.dc.publisher[].value
Society of Photo-Optical Instrumentation Engineers (SPIE)

# metadata.dc.rights[].value
(c) Society of Photo-Optical Instrumentation Engineers (SPIE), 2017

# metadata.dc.source[].value
Articles publicats en revistes (Física Aplicada)

# metadata.dc.title[].value
Validation of optical codes based on 3D nanostructures