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Electronics
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Compressive Optical Image Encryption
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Compressive Optical Image Encryption

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (20 January 2022) | Viewed by 16081

Special Issue Editors

Prof. Dr. Ayman Alfalou

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Guest Editor
L@bisen, Yncréa Ouest, 20 Rue Cuirassé Bretagne, C.S. 42807, CEDEX 2, 29228 Brest, France
Interests: image/video/point cloud processing; 2D/3D/hyperspectral image segmentation; NLP
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Saad Rehman

E-Mail Website
Guest Editor
Faculty of Computer Engineering, HITEC University, Taxila 47080, Pakistan
Interests: pattern recognition; image and signal processing
Dr. Marwa Elbouz

E-Mail
Guest Editor
LSL Team, L@bisen, Yncréa Ouest, 20 Rue Cuirassé Bretagne, C.S. 42807, CEDEX 2, 29228 Brest, France
Interests: encryption; translation; face recognition; signal, image and video processing; cryptography
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

New data-processing network systems and new image processing tools have considerably increased the volume of transmitted information. Thus, more complex networks and long processing time become necessary. High image quality and transmission speed are requested for an increasing number of applications. To satisfy these two requests, several either numerical or optical solutions have been offered separately. This Special Issue explores both alternatives and gives particular consideration to research works on the nature to converge towards optical/numerical hybrid solutions for high-volume signal and image processing and transmission. Without being limited to hybrid approaches, the latter are particularly investigated in this Special Issue with the purpose of combining the advantages of both techniques. Pure numerical or optical solutions are also considered since they emphasize the advantages of one of the two approaches separately.

This Special Issue will gather a series of articles dealing with compressive optical image encryption. These topics highlight the importance of studying complex data treatment systems and intensive calculations designed for high-dimensional imaging and metrology for which high image quality and high transmission speed become critical issues in a number of technological applications. A second purpose is to highlight the important role of optics in actual information processing systems.

The session topics include but are not limited to the following areas:

  • Optical/digital cryptography;
  • Optical/digital watermarking;
  • Optical/digital biometrics;
  • Compressive image;
  • Compressive sensing for images;
  • Compressive optical image encryption;
  • Image compression for the medical image processing domain;
  • Optical cryptanalysis;
  • Novel devices and systems for optical information security;
  • Face/fingerprint/iris/person recognition, person tracking, 3D object recognition;
  • Liveness detection for face recognition;
  • Holography, phase imaging, and their applications;
  • Integral imaging;
  • Devices and systems for 3D imaging and display;
  • Polarization imaging, hyperspectral imaging;
  • Remote sensing: aerial and satellite image interpretations;
  • Industrial applications (e.g., product inspection/sorting).

Prof. Dr. Ayman Alfalou
Prof. Dr. Saad Rehman
Dr. Marwa Elbouz
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (6 papers)

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Research

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30 pages, 11063 KiB  
Article
Designing 1D Chaotic Maps for Fast Chaotic Image Encryption
by Mustafa Kamil Khairullah, Ammar Ahmed Alkahtani, Mohd Zafri Bin Baharuddin and Ammar Mohammed Al-Jubari
Electronics 2021, 10(17), 2116; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10172116 - 31 Aug 2021
Cited by 28 | Viewed by 3558
Abstract
Chaotic maps that can provide highly secure key sequences and ease of structure implementation are predominant requirements in image encryption systems. One Dimensional (1D) chaotic maps have the advantage of a simple structure and can be easily implemented by software and hardware. However, [...] Read more.
Chaotic maps that can provide highly secure key sequences and ease of structure implementation are predominant requirements in image encryption systems. One Dimensional (1D) chaotic maps have the advantage of a simple structure and can be easily implemented by software and hardware. However, key sequences produced by 1D chaotic maps are not adequately secure. Therefore, to improve the 1D chaotic maps sequence security, we propose two chaotic maps: 1D Improved Logistic Map (1D-ILM) and 1D Improved Quadratic Map (1D-IQM). The proposed maps have shown higher efficiency than existing maps in terms of Lyapunov exponent, complexity, wider chaotic range, and higher sensitivity. Additionally, we present an efficient and fast encryption method based on 1D-ILM and 1D-IQM to enhance image encryption system performance. This paper also introduces a key expansion method to reduce the number of chaotic map iteration needs, thereby decreasing encryption time. The security analyses and experimental results are confirmed that 2D Correlation Coefficient (CC) Information Entropy (IE), Number of Pixels Change Rate (NPCR), Unified Average Changing Intensity (UACI), Mean Absolute Error (MAE), and decryption quality are able to meet the encryption security demands (CC = −0.00139, IE = 7.9990, NPCR = 99.6114%, UACI = 33.46952% and MAE = 85.3473). Furthermore, the proposed keyspace reaches 10240, and the encryption time is 0.025s for an image with a size of 256 × 256. The proposed system can yield efficacious security results compared to obtained results from other encryption systems. Full article
(This article belongs to the Special Issue Compressive Optical Image Encryption)
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15 pages, 2915 KiB  
Article
HEVC’s Intra Mode Selection Using Odds Algorithm
by Ammar Armghan, Junaid Tariq, Fayadh Alenezi, Norah Alnaim, Ayman Alfalou and Saad Rehman
Electronics 2021, 10(15), 1839; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10151839 - 31 Jul 2021
Cited by 3 | Viewed by 1764
Abstract
The brute-force behaviour of High-Efficiency Video Coding (HEVC) is the biggest hurdle in the communication of the multimedia contents. Therefore, a novel method will be presented here to expedite the intra mode decision process of HEVC. In the first step, the feasibility of [...] Read more.
The brute-force behaviour of High-Efficiency Video Coding (HEVC) is the biggest hurdle in the communication of the multimedia contents. Therefore, a novel method will be presented here to expedite the intra mode decision process of HEVC. In the first step, the feasibility of the odds-algorithm for the early intra mode decision is presented by using statistical evidence. Then, various elements of odds algorithm are analyzed and then mapped to the intra mode process (elements) of HEVC. Finally, the probability required by the odds algorithm is obtained by utilizing the correlation between the current and the neighboring blocks. The proposed algorithm accelerated the encoding process of the HEVC by 25% to 35%, while the Bjontegaard Delta Bit Rate (BD-BR) is 0.95% to 1.84%. Full article
(This article belongs to the Special Issue Compressive Optical Image Encryption)
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16 pages, 2604 KiB  
Article
HEVC Fast Intra-Mode Selection Using World War II Technique
by Junaid Tariq, Ammar Armghan, Fayadh Alenezi, Amir Ijaz, Saad Rehman, Ayman Alfalou and Junaid Ali Khan
Electronics 2021, 10(9), 985; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10090985 - 21 Apr 2021
Cited by 4 | Viewed by 1707
Abstract
High-Efficiency Video Coding (HEVC) applies 35 intra modes to every block of a frame and selects the mode that gives the best prediction. This brute-force nature of HEVC makes it complex and unfit for real-time applications. Therefore, a fast intra-mode estimation algorithm is [...] Read more.
High-Efficiency Video Coding (HEVC) applies 35 intra modes to every block of a frame and selects the mode that gives the best prediction. This brute-force nature of HEVC makes it complex and unfit for real-time applications. Therefore, a fast intra-mode estimation algorithm is presented here based on the classic World War II (WW2) technique known as the ‘German Tanks Problem’ (GTP). This not only is the first article to use GTP for early estimation of intra mode, but also expedites the estimation process of GTP. Secondly, the various elements of the intra process are efficiently mapped to the elements of GTP estimation. Finally, the two variations of GPT are modeled and are also minimum-variance estimates. These experimental results indicate that proposed GTP-based fast estimation reduced the compression time of HEVC from 23.88% to 31.44%. Full article
(This article belongs to the Special Issue Compressive Optical Image Encryption)
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13 pages, 2632 KiB  
Article
Polarization-Encoded Fully-Phase Encryption Using Transport-of-Intensity Equation
by Alok K. Gupta, Praveen Kumar, Naveen K. Nishchal and Ayman Alfalou
Electronics 2021, 10(8), 969; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10080969 - 19 Apr 2021
Cited by 6 | Viewed by 2045
Abstract
In this study, we propose a novel method to encrypt fully-phase information combining the concepts of the transport of intensity equation and spatially variant polarization encoding. The transport of intensity equation is a non-iterative and non-interferometric phase-retrieval method which recovers the phase information [...] Read more.
In this study, we propose a novel method to encrypt fully-phase information combining the concepts of the transport of intensity equation and spatially variant polarization encoding. The transport of intensity equation is a non-iterative and non-interferometric phase-retrieval method which recovers the phase information from defocused intensities. Spatially variant polarization encoding employs defocused intensity measurements. The proposed cryptosystem uses a two-step optical experimentation process—primarily, a simple set-up for defocused intensities recording for phase retrieval and then a set-up for encoding. Strong security, convenient intensity-based measurements, and noise-free decryption are the main features of the proposed method. The simulation results have been presented in support of the proposed idea. However, the TIE section of the cryptosystem, as of now, has been experimentally demonstrated for micro-lens. Full article
(This article belongs to the Special Issue Compressive Optical Image Encryption)
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44 pages, 19965 KiB  
Article
Weight Quantization Retraining for Sparse and Compressed Spatial Domain Correlation Filters
by Dilshad Sabir, Muhammmad Abdullah Hanif, Ali Hassan, Saad Rehman and Muhammad Shafique
Electronics 2021, 10(3), 351; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10030351 - 02 Feb 2021
Cited by 1 | Viewed by 2383
Abstract
Using Spatial Domain Correlation Pattern Recognition (CPR) in Internet-of-Things (IoT)-based applications often faces constraints, like inadequate computational resources and limited memory. To reduce the computation workload of inference due to large spatial-domain CPR filters and convert filter weights into hardware-friendly data-types, this paper [...] Read more.
Using Spatial Domain Correlation Pattern Recognition (CPR) in Internet-of-Things (IoT)-based applications often faces constraints, like inadequate computational resources and limited memory. To reduce the computation workload of inference due to large spatial-domain CPR filters and convert filter weights into hardware-friendly data-types, this paper introduces the power-of-two (Po2) and dynamic-fixed-point (DFP) quantization techniques for weight compression and the sparsity induction in filters. Weight quantization re-training (WQR), the log-polar, and the inverse log-polar geometric transformations are introduced to reduce quantization error. WQR is a method of retraining the CPR filter, which is presented to recover the accuracy loss. It forces the given quantization scheme by adding the quantization error in the training sample and then re-quantizes the filter to the desired quantization levels which reduce quantization noise. Further, Particle Swarm Optimization (PSO) is used to fine-tune parameters during WQR. Both geometric transforms are applied as pre-processing steps. The Po2 quantization scheme showed better performance close to the performance of full precision, while the DFP quantization showed further closeness to the Receiver Operator Characteristic of full precision for the same bit-length. Overall, spatial-trained filters showed a better compression ratio for Po2 quantization after retraining of the CPR filter. The direct quantization approach achieved a compression ratio of 8 at 4.37× speedup with no accuracy degradation. In contrast, quantization with a log-polar transform is accomplished at a compression ratio of 4 at 1.12× speedup, but, in this case, 16% accuracy of degradation is noticed. Inverse log-polar transform showed a compression ratio of 16 at 8.90× speedup and 6% accuracy degradation. All the mentioned accuracies are reported for a common database. Full article
(This article belongs to the Special Issue Compressive Optical Image Encryption)
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Review

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13 pages, 459 KiB  
Review
Privacy-Preserving Deep Neural Network Methods: Computational and Perceptual Methods—An Overview
by Raghida El Saj, Ehsan Sedgh Gooya, Ayman Alfalou and Mohamad Khalil
Electronics 2021, 10(11), 1367; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10111367 - 07 Jun 2021
Cited by 9 | Viewed by 3444
Abstract
Privacy-preserving deep neural networks have become essential and have attracted the attention of many researchers due to the need to maintain the privacy and the confidentiality of personal and sensitive data. The importance of privacy-preserving networks has increased with the widespread use of [...] Read more.
Privacy-preserving deep neural networks have become essential and have attracted the attention of many researchers due to the need to maintain the privacy and the confidentiality of personal and sensitive data. The importance of privacy-preserving networks has increased with the widespread use of neural networks as a service in unsecured cloud environments. Different methods have been proposed and developed to solve the privacy-preserving problem using deep neural networks on encrypted data. In this article, we reviewed some of the most relevant and well-known computational and perceptual image encryption methods. These methods as well as their results have been presented, compared, and the conditions of their use, the durability and robustness of some of them against attacks, have been discussed. Some of the mentioned methods have demonstrated an ability to hide information and make it difficult for adversaries to retrieve it while maintaining high classification accuracy. Based on the obtained results, it was suggested to develop and use some of the cited privacy-preserving methods in applications other than classification. Full article
(This article belongs to the Special Issue Compressive Optical Image Encryption)
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