Optical image cryptosystem using chaotic phase-amplitude masks encoding and least-data-driven decryption by compressive sensing

Lang, Jun; Zhang, Jing

doi:10.1016/j.optcom.2014.10.018

Cited by 14 publications

(4 citation statements)

References 28 publications

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“…Compressive sensing (CS), also referred to sparse signal sampling, was introduced by work of Donoho, Candes [157,158]. CS is able to achieve compression and signal sampling simultaneously [118,159,160]. For a signal of bandwidth, BW = , the sampling frequency (f s ) required to represent the signal is much smaller than Nyquist frequency (f s ≪ ).…”

Section: Compressive Sensing (T )mentioning

confidence: 99%

Image encryption using fractional integral transforms: Vulnerabilities, threats, and future scope

Kaur

Agarwal

Patidar

2022

Front. Appl. Math. Stat.

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With the enormous usage of digital media in almost every sphere from education to entertainment, the security of sensitive information has been a concern. As images are the most frequently used means to convey information, the issue related to the privacy preservation needs to be addressed in each of the application domains. There are various security methods proposed by researchers from time to time. This paper presents a review of various image encryption schemes based on fractional integral transform. As the fractional integral transforms have evolved through their applications from optical signal processing to digital signal and digital image processing over the decades. In this article, we have adopted an architecture and corresponding domain-based taxonomy to classify various existing schemes in the literature. The schemes are classified according to the implementation platform, that may be an optical setup comprising of the spatial modulators, lenses, and charge-coupled devices or it can be a mathematical modeling of such transforms. Various schemes are classified according to the methodology adopted in each of them and a comparative analysis is also presented in tabular form. Based on the observations, the work is converged into a summary of various challenges and some constructive guidelines are provided for consideration in future works. Such a narrative review of encryption algorithm based on various architectural schematics in fractional integral transforms has not been presented before at one place.

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Section: Compressive Sensing (T )mentioning

confidence: 99%

Image encryption using fractional integral transforms: Vulnerabilities, threats, and future scope

Kaur

Agarwal

Patidar

2022

Front. Appl. Math. Stat.

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“…In [36], the measurement matrix and the random phase masks are constituted by chaotic sequences. Lang and Zhang came up with a unique perspective of transmitting only a few measurements intermittently chosen from the masks rather than the real keys and the tremendous masks codes [37], since the parameters of the chaotic maps can be inferred from the received measurements without error so that the correct random phase-amplitude masks can be obtained and used for decrypting the encoded information. The further review of image encryption based on CS is shown in the following sections.…”

Section: Application Security a Image Security 1) Image Encryptionmentioning

confidence: 99%

“…In fact, there also exist a framework of image ciphers based on CS itself, as shown in [39]- [42], which combine neither chaos nor optics. Finally, what is worth mentioning is a particular image cryptosystem using chaotic phaseamplitude masks and least-data-driven decryption by CS [37]. The highlight lies in that it transmits only a few measurements intermittently chosen from the masks rather than the real keys and the enormous mask codes.…”

Section: Image Ciphers Based On Chaos Optics and Csmentioning

confidence: 99%

A Review of Compressive Sensing in Information Security Field

Zhang

Zhou³

et al. 2016

IEEE Access

169

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The applications of compressive sensing (CS) in the field of information security have captured a great deal of researchers' attention in the past decade. To supply guidance for researchers from a comprehensive perspective, this paper, for the first time, reviews CS in information security field from two aspects: theoretical security and application security. Moreover, the CS applied in image cipher is one of the most widespread applications, as its characteristics of dimensional reduction and random projection can be utilized and integrated into image cryptosystems, which can achieve simultaneous compression and encryption of an image or multiple images. With respect to this application, the basic framework designs and the corresponding analyses are investigated. Specifically, the investigation proceeds from three aspects, namely, image ciphers based on chaos and CS, image ciphers based on optics and CS, and image ciphers based on chaos, optics, and CS. A total of six frameworks are put forward. Meanwhile, their analyses in terms of security, advantages, disadvantages, and so on are presented. At last, we attempt to indicate some other possible application research topics in future.

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“…It has been also applied for spectrometry [25], Stokes polarimetric imaging [26] and hyperspectral imag-ing [27,28], and holography [29,30,31]. Image encryption with compression is another major application of CS [32,33,34,35,36,37]. Ref.…”

Section: Introductionmentioning

confidence: 99%

Compressive phase-only filtering at extreme compression rates

Pastor-Calle

Pastuszczak

Mikołajczyk

et al. 2017

Optics Communications

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We introduce an efficient method for the reconstruction of the correlation between a compressively measured image and a phase-only filter. The proposed method is based on two properties of phase-only filtering: such filtering is a unitary circulant transform, and the correlation plane it produces is usually sparse. Thanks to these properties, phase-only filters are perfectly compatible with the framework of compressive sensing. Moreover, the lasso-based recovery algorithm is very fast when phase-only filtering is used as the compression matrix. The proposed method can be seen as a generalisation of the correlation-based pattern recognition technique, which is hereby applied directly to non-adaptively acquired compressed data. At the time of measurement, any prior knowledge of the target object for which the data will be scanned is not required. We show that images measured at extremely high compression rates may still contain sufficient information for target classification and localization, even if the compression rate is high enough, that visual recognition of the target in the reconstructed image is no longer possible. The method has been applied by us to highly undersampled measurements obtained from a single-pixel camera, with sampling based on randomly chosen Walsh-Hadamard patterns.

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Optical image cryptosystem using chaotic phase-amplitude masks encoding and least-data-driven decryption by compressive sensing

Cited by 14 publications

References 28 publications

Image encryption using fractional integral transforms: Vulnerabilities, threats, and future scope

Image encryption using fractional integral transforms: Vulnerabilities, threats, and future scope

A Review of Compressive Sensing in Information Security Field

Compressive phase-only filtering at extreme compression rates

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