Optical security and authentication using nanoscale and thin-film structures

Carnicer, Artur; Javidi, Bahram

doi:10.1364/aop.9.000218

Cited by 48 publications

(19 citation statements)

References 89 publications

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“…This allows for efficient video and data transmission through fibres and promises applications beyond endoscopic imaging, such as direct video or multimode data transmission over long fibres for communication systems and fibre sensing by, for example, exploiting the image sensitivity to changes along the fibre length. Moreover, one could also exploit the intrinsic random nature of the multimode coupling and output speckle patterns to securely encode and authenticate data as proposed recently [30,31] where the transmission fibre itself would play the role of the encoding medium, with possible extensions also to the control of quantum states for quantum sensing and simulation [32].…”

Section: Discussionmentioning

confidence: 99%

Transmission of natural scene images through a multimode fibre

et al. 2019

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The optical transport of images through a multimode fibre remains an outstanding challenge with applications ranging from optical communications to neuro-imaging. State of the art approaches either involve measurement and control of the full complex field transmitted through the fibre or, more recently, training of artificial neural networks that however, are typically limited to image classes belong to the same class as the training data set. Here we implement a method that statistically reconstructs the inverse transformation matrix for the fibre. We demonstrate imaging at high frame rates, high resolutions and in full colour of natural scenes, thus demonstrating general-purpose imaging capability. Real-time imaging over long fibre lengths opens alternative routes to exploitation for example for secure communication systems, novel remote imaging devices, quantum state control processing and endoscopy.

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Section: Discussionmentioning

confidence: 99%

Transmission of natural scene images through a multimode fibre

et al. 2019

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“…3(a-c) and 3(d-f), respectively. Note that in this case, the profile of the histograms clearly follows a Gamma probability distribution [14][15][16], i.e.…”

Section: A Numerical Model For 3d Random Phase Codesmentioning

confidence: 99%

Validation of optical codes based on 3D nanostructures

Carnicer¹,

Javidi²

2017

SPIE Proceedings

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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.

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“…Optical encryption has captured growing attentions in the past two decades owning to its inherent advantages such as parallel signal processing and high dimensional operation 1,2 . When irradiating a two-dimensional image vertically with parallel light, all points on the image will be modulated by light simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Deep-learning-based ciphertext-only attack on optical double random phase encryption

Liao¹,

Zheng²,

Pan³

et al. 2021

OEA

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Optical cryptanalysis is essential to the further investigation of more secure optical cryptosystems. Learning-based attack of optical encryption eliminates the need for the retrieval of random phase keys of optical encryption systems but it is limited for practical applications since it requires a large set of plaintext-ciphertext pairs for the cryptosystem to be attacked. Here, we propose a two-step deep learning strategy for ciphertext-only attack (COA) on the classical double random phase encryption (DRPE). Specifically, we construct a virtual DRPE system to gather the training data. Besides, we divide the inverse problem in COA into two more specific inverse problems and employ two deep neural networks (DNNs) to respectively learn the removal of speckle noise in the autocorrelation domain and the de-correlation operation to retrieve the plaintext image. With these two trained DNNs at hand, we show that the plaintext can be predicted in realtime from an unknown ciphertext alone. The proposed learning-based COA method dispenses with not only the retrieval of random phase keys but also the invasive data acquisition of plaintext-ciphertext pairs in the DPRE system. Numerical simulations and optical experiments demonstrate the feasibility and effectiveness of the proposed learning-based COA method.

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Optical security and authentication using nanoscale and thin-film structures

Cited by 48 publications

References 89 publications

Transmission of natural scene images through a multimode fibre

Transmission of natural scene images through a multimode fibre

Validation of optical codes based on 3D nanostructures

Deep-learning-based ciphertext-only attack on optical double random phase encryption

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