Secure Image Transmission Research Articles

Secure image transmission through an OFDM and MIMO OFDM systems with different cryptographic algorithms (2020)

Secure image transmission through an OFDM and MIMO OFDM systems with different cryptographic algorithms (2020)

Integration of lightweight cryptography and watermarking with compression for high speed and reliable communication of digital images in IoT

AbstractThe Internet of Things (IoT) has grown rapidly over the past few years. The main challenges for people who design and make these systems are to make them use less energy and use as small hardware as possible. Recording and sending compressed images in IoT systems must be secure. Secure image transmission requires encryption, watermarking, and compression. In choosing these algorithms, one should always consider energy efficiency and low hardware complexity. So, we propose a secure way to send data in IoT systems by combining encryption, watermarking, and compression. Depending on what is needed, we use a lightweight encryption algorithm, discrete wavelet transform (DWT) watermarking, and Discrete cosine transform + Lempel‐Ziv‐Welch (DCT+LZW) compression. In this method, the main contribution is to make a security pack that is small, strong, and easy to implement for secure image transmission on IoT systems. In the experiments, several images are used to judge how well the proposed integrated method works and how well it compares to other methods. When the proposed method was compared to other methods, the peak signal‐to‐noise ratio (PSNR) parameter improved by more than 20% in the Lena image and by more than 11% in the Baboon image.

FPGA Implementation of Parameter-Switching Scheme to Stabilize Chaos in Fractional Spherical Systems and Usage in Secure Image Transmission

The main objective of this work was to implement the parameter-switching chaos control scheme for fractional-order spherical systems and develop a chaos-based image encryption and transmission system. The novelty in the developed secure communication system is the application of the parameter-switching scheme in the decryption of RGB and grayscale images, which undergo one round of encryption using the chaotic states of the fractional system and a diffusion process. The secure communication system has a synchronized master and slave topology, resulting in transmitter and receiver systems for encrypting and decrypting images, respectively. This work was demonstrated numerically and also implemented on two FPGAs, namely Artix-7 AC701 and Cyclone V. The results show that the parameter-switching scheme controls chaos in the fractional-order spherical systems effectively. Furthermore, the performance analysis of the image encryption and transmission system shows that there is no similarity between the original and encrypted images, while the decryption of the encrypted images is without a loss of quality. The best result in terms of the encryption was obtained from the chaotic state x of the fractional-order system, with correlation coefficients of 0.0511 and 0.0392 for the RGB and grayscale images, respectively. Finally, the utilization of the FPGA logical resources shows that the implementation on Artix-7 AC701 is more logic-efficient than on Cyclone V.

Internet of Medical Things with a Blockchain-Assisted Smart Healthcare System Using Metaheuristics with a Deep Learning Model

The Internet of Medical Things (IoMT) is a network of healthcare devices such as wearables, diagnostic equipment, and implantable devices, which are linked to the internet and can communicate with one another. Blockchain (BC) technology can design a secure, decentralized system to store and share medical data in an IoMT-based intelligent healthcare system. Patient records were stored in a tamper-proof and decentralized way using BC, which provides high privacy and security for the patients. Furthermore, BC enables efficient and secure sharing of healthcare data between patients and health professionals, enhancing healthcare quality. Therefore, in this paper, we develop an IoMT with a blockchain-based smart healthcare system using encryption with an optimal deep learning (BSHS-EODL) model. The presented BSHS-EODL method allows BC-assisted secured image transmission and diagnoses models for the IoMT environment. The proposed method includes data classification, data collection, and image encryption. Initially, the IoMT devices enable data collection processes, and the gathered images are stored in BC for security. Then, image encryption is applied for data encryption, and its key generation method can be performed via the dingo optimization algorithm (DOA). Finally, the BSHS-EODL technique performs disease diagnosis comprising SqueezeNet, Bayesian optimization (BO) based parameter tuning, and voting extreme learning machine (VELM). A comprehensive set of simulation analyses on medical datasets highlights the betterment of the BSHS-EODL method over existing techniques with a maximum accuracy of 98.51%, whereas the existing methods such as DBN, YOLO-GC, ResNet, VGG-19, and CDNN models have lower accuracies of 94.15%, 94.24%, 96.19%, 91.19%, and 95.29% respectively.

An efficient hybrid fuzzy image encryption models for the secured cloud accessing in portable robotics devices

Secure sharing of image data through different communication channels is a challenging research issue in digital networks. In such cases, cryptography algorithms are widely used to encrypt and decrypt the information reliably at both ends of source and target destinations. Various state-of-the-art image encryption algorithms are surveyed and investigated to assess the best image encryption. In this paper, image encryption is described with various fuzzy operation modes. It ensures secure image transmission over to the digital channels. Most reliable image encryption is the aim of the work, and it is achieved through developing a hybrid fuzzy-based framework that composites the techniques of image encryption methods and fuzzy operation modes. Proposed hybrid fuzzy-based models are highly recommended to maintain secure information on portable robotics devices, communication channels, and routing devices. These hybrid encrypted models provide the users' privacy to access the image data with reliable security mechanisms.

W-VDSR: wavelet-based secure image transmission using machine learning VDSR neural network

W-VDSR: wavelet-based secure image transmission using machine learning VDSR neural network

One-Dimensional Quadratic Chaotic System and Splicing Model for Image Encryption

Digital image transmission plays a very significant role in information transmission, so it is very important to protect the security of image transmission. Based on the analysis of existing image encryption algorithms, this article proposes a new digital image encryption algorithm based on the splicing model and 1D secondary chaotic system. Step one is the algorithm of this article divides the plain image into four sub-parts by using quaternary coding, and these four sub-parts can be coded separately. Only by acquiring all the sub-parts at one time can the attacker recover the useful plain image. Therefore, the algorithm has high security. Additionally, the image encryption scheme in this article used a 1D quadratic chaotic system, which makes the key space big enough to resist exhaustive attacks. The experimental data show that the image encryption algorithm has high security and a good encryption effect.

High Level Security of Image Transmission through STBC-COFDM System

High Level Security of Image Transmission through STBC-COFDM System

Privacy Preserving Image Encryption with Optimal Deep Transfer Learning Based Accident Severity Classification Model.

Effective accident management acts as a vital part of emergency and traffic control systems. In such systems, accident data can be collected from different sources (unmanned aerial vehicles, surveillance cameras, on-site people, etc.) and images are considered a major source. Accident site photos and measurements are the most important evidence. Attackers will steal data and breach personal privacy, causing untold costs. The massive number of images commonly employed poses a significant challenge to privacy preservation, and image encryption can be used to accomplish cloud storage and secure image transmission. Automated severity estimation using deep-learning (DL) models becomes essential for effective accident management. Therefore, this article presents a novel Privacy Preserving Image Encryption with Optimal Deep-Learning-based Accident Severity Classification (PPIE-ODLASC) method. The primary objective of the PPIE-ODLASC algorithm is to securely transmit the accident images and classify accident severity into different levels. In the presented PPIE-ODLASC technique, two major processes are involved, namely encryption and severity classification (i.e., high, medium, low, and normal). For accident image encryption, the multi-key homomorphic encryption (MKHE) technique with lion swarm optimization (LSO)-based optimal key generation procedure is involved. In addition, the PPIE-ODLASC approach involves YOLO-v5 object detector to identify the region of interest (ROI) in the accident images. Moreover, the accident severity classification module encompasses Xception feature extractor, bidirectional gated recurrent unit (BiGRU) classification, and Bayesian optimization (BO)-based hyperparameter tuning. The experimental validation of the proposed PPIE-ODLASC algorithm is tested utilizing accident images and the outcomes are examined in terms of many measures. The comparative examination revealed that the PPIE-ODLASC technique showed an enhanced performance of 57.68 dB over other existing models.

E-Commerce Transaction Using Visual Cryptography

The abstract focuses on the concept of e-commerce transactions using visual cryptography. E-commerce has revolutionized the way businesses operate and has become an integral part of our daily lives. However, the security of online transactions remains a critical concern. Visual cryptography is a technique that allows the secure transmission of images or information through encryption and decryption processes. This abstract explores the potential of visual cryptography in enhancing the security of e-commerce transactions. It discusses the advantages and challenges of using visual cryptography and proposes a framework for implementing this technique in e-commerce platforms. The abstract concludes by highlighting the potential benefits of visual cryptography in ensuring secure and trustworthy e-commerce transactions.

A Self-Adaptive Image Encryption Scheme Based on Chaos and Gravitation Model

In order to improve the security of image transmission, this paper proposes a self-adaptive image encryption scheme based on chaos and gravitation model. The proposed scheme consists of three stages: key generation stage, double scrambling stage, and double diffusion stage. In the key generation stage, the plaintext-associated key generation mechanism (BPPAKG) based on the bit plane is used to obtain the key associated with the plaintext. Since the key ultimately depends on the plaintext image itself, this scheme has self-adaptability. The first stage of scrambling uses mixed chaotic transform and shift row transform to disturb pixel position, then changes the pixel value of the image based on the concept of gravitation to achieve the first stage of diffusion. To enhance the security performance of the scheme, the traditional encryption structure is extended, and the second stage of scrambling and diffusion is quickly realized by using diagonal scanning transform and XOR operation. Simulation and experimental results show that the scheme has good encryption and decryption performance and strong resistance to attack. In addition, compared with some latest proposed algorithms, it has better security.

Chaos and DNA Blended Hybrid Encryption Algorithm for Secure Image Transmission over DCT Pre-coded OFDM

Chaos and DNA Blended Hybrid Encryption Algorithm for Secure Image Transmission over DCT Pre-coded OFDM

FPGA based secure and noiseless image transmission using LEA and optimized bilateral filter

In today’s world, the transmission of secured and noiseless image is a difficult task. Therefore, effective strategies are important to secure the data or secret image from the attackers. Besides, denoising approaches are important to obtain noise-free images. For this, an effective crypto-steganography method based on Lightweight Encryption Algorithm (LEA) and Modified Least Significant Bit (MLSB) method for secured transmission is proposed. Moreover, a bilateral filter-based Whale Optimization Algorithm (WOA) is used for image denoising. Before image transmission, the secret image is encrypted by the LEA algorithm and embedded into the cover image using Discrete Wavelet Transform (DWT) and MLSB technique. After the image transmission, the extraction process is performed to recover the secret image. Finally, a bilateral filter-WOA is used to remove the noise from the secret image. The Verilog code for the proposed model is designed and simulated in Xilinx software. Finally, the simulation results show that the proposed filtering technique has superior performance than conventional bilateral filter and Gaussian filter in terms of Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM).

Image encryption based on Logistic-Sine self-embedding chaotic sequence

In this article, for the security of image transmission, we present a novel image encryption algorithm based on Logistic-Sine self-embedding (LSSE) chaotic sequence. Firstly, we verify the effectiveness and superiority of the chaotic map by using a variety of indicators such as the Lyapunov exponent, bifurcation diagram, and 0-1 test. The chaotic key is combined with Arnold scrambling transformation to scramble the image, to break the high correlation between image pixels. Then, the image is diffused based on LSSE. The robustness and effectiveness of the proposed method are verified by various computational tests, and the scheme is analyzed using the performance indicators of histogram distribution, correlation, key sensitivity, attack test, and comparison with other algorithms. The computational experiments show that the algorithm has excellent performance and adequate security.

A smartphone lens attachment improves the quality of referrals to eye casualty.

Background/objectivesIn recent years, eye casualty clinics have seen significant increases in patient numbers with reduced capacity. COVID-19 has exacerbated this issue and demonstrated the potential of telemedicine as a solution. Our study evaluated the potential benefit of a smartphone-based lens attachment to improve the referral pathway for anterior segment related complaints in eye casualty.Subjects/methodsFifty-four consecutive patients with anterior segment complaints were recruited. A questionnaire was completed with each patient to simulate the history from the point of referral. White light and cobalt blue photos were captured using a smartphone lens. The clinician reviewing the patient was asked to document the actual diagnosis and the appropriate time-frame within which they felt the patient could safely have been seen within; both with and without the option of management advice at the time of triage. The subsequent images and questionnaires were reviewed by a single consultant Ophthalmologist who was independent to the data collection process. The assessor was asked to make a diagnosis and management plan based upon the questionnaire (‘History’), and the questionnaire with the photo (‘History with Image’), as well as rate their clinical confidence on a 1–5 scale.ResultsDiagnostic accuracy was significantly higher in “History with Image” (98.2%), when compared to “History” only (48.2%). “History with Image” prevented significantly more appointments when compared to “History” alone, at similar levels to retrospective clinic review. Preventable appointments were increased if clinical advice was possible. Timeframe of appointments between ‘History with Image’ and ‘Clinic’ appointments was similar. Clinical’Confidence was significantly higher at 4.5 with ‘History with Image’ when compared to 2.37 with ‘History Only’.ConclusionA low-cost smartphone lens attachment, alongside a standardised clinical questionnaire, can improve the referral pathway to the hospital eye service by reducing unnecessary appointments, while improving clinical confidence and diagnostic accuracy during triage. Further work to evaluate referral pathways, including the development of systems that allow for secure image transmission are needed to understand the feasibility for the widespread adoption of this technology.

Secure Image Encryption Using Chaotic, Hybrid Chaotic and Block Cipher Approach.

Secure image transmission is one of the most challenging problems in the age of communication technology. Millions of people use and transfer images for either personal or commercial purposes over the internet. One way of achieving secure image transmission over the network is encryption techniques that convert the original image into a non-understandable or scrambled form, called a cipher image, so that even if the attacker gets access to the cipher they would not be able to retrieve the original image. In this study, chaos-based image encryption and block cipher techniques are implemented and analyzed for image encryption. Arnold cat map in combination with a logistic map are used as native chaotic and hybrid chaotic approaches respectively whereas advanced encryption standard (AES) is used as a block cipher approach. The chaotic and AES methods are applied to encrypt images and are subjected to measures of different performance parameters such as peak signal to noise ratio (PSNR), number of pixels change rate (NPCR), unified average changing intensity (UACI), and histogram and computation time analysis to measure the strength of each algorithm. The results show that the hybrid chaotic map has better NPCR and UACI values which makes it more robust to differential attacks or chosen plain text attacks. The Arnold cat map is computationally efficient in comparison to the other two approaches. However, AES has a lower PSNR value (7.53 to 11.93) and has more variation between histograms of original and cipher images, thereby indicating that it is more resistant to statistical attacks than the other two approaches.

Multi-Image Compression-Encryption Algorithm Based on Compressed Sensing and Optical Encryption.

In order to achieve large-capacity, fast and secure image transmission, a multi-image compression–encryption algorithm based on two-dimensional compressed sensing (2D CS) and optical encryption is proposed in this paper. Firstly, the paper uses compressed sensing to compress and encrypt multiple images simultaneously, and design a new structured measurement matrix. Subsequently, double random phase encoding based on the multi-parameter fractional quaternion Fourier transform is used to encrypt the multiple images for secondary encryption, which improves the security performance of the images. Moreover, a fractional-order chaotic system with more complex chaotic behavior is constructed for image compression and encryption. Experimental results show that the algorithm has strong robustness and security.

Blockchain enabled optimal Hopfield Chaotic Neural network based secure encryption technique for industrial internet of things environment

Industrial Internet of Things (IIoT) denotes a network of interlinked sensors, instruments, and other devices for industrial applications in the domains of manufacturing, logistics, transportation, etc. IIoT security is a major crucial research area for several applications. Image encryption techniques gained popularity in the recent years, thanks to increasing requirements for secure image transmission in IIoT environments. At the same time, conventional security solutions built for sensitive data protection are getting outdated in IIoT environment due to the participation of third party. Blockchain (BC) is one of the recent solutions used for security purpose which eliminates the involvement of a third party. With this motivation, the current research article presents a new BC-Enabled Shark Smell Optimization with Hopfield Chaotic Neural Network (SSO-HCNN) for secure encryption in IoT environment. The proposed SSO-HCNN model exploits a composite Chaotic Map (CM) which is integrated into staged logistic and tent maps to initially process the images and develop the variables needed for Arnold mapping. In addition, the SSO algorithm is developed with maximum PSNR and coefficient fitness function to select the optimum secret and public keys of the system amongst the random numbers. Besides, the diffusion phase utilizes HCNN to create a self-diffusion chaotic matrix whereas the jumbled image performs XOR operation using the keys to obtain the cipher image. In SSO-HCNN model, the cryptographic pixel value in the image is saved on BC thus guaranteeing the security and privacy of the images. To examine the superior performance of SSO-HCNN model over state-of-the-art methods, a set of simulations was conducted on benchmark test images. The simulation results of the proposed SSO-HCNN model were promising under different evaluation parameters.

(Retracted) Blockchain enabled secure image transmission and diagnosis scheme in medical cyber-physical systems

Medical cyber-physical systems (MCPSs) are life critical, context aware, networked systems of medical devices that are increasingly used in hospitals to achieve seamless high-quality healthcare. The design of the MCPS for the healthcare sector necessitates significant attention to achieving security. As the medical images need to be communicated regularly for timely and accurate diagnosis, medical images need to be secured by encryption and blockchain technologies. In this aspect, we present a blockchain enabled secure image transmission and diagnosis (BESITD) for the MCPS environment. The BESITD technique encompasses an image acquisition process that enables the wearable devices to capture the medical images. Then, the presented model executes an intrusion detection system using recurrent neural network to determine the presence of intruders in the MCPS. In addition, the block-wise encryption process takes place in which the medical image is partitioned into n blocks, each of which is individually encrypted using the signcryption technique. Moreover, a consortium blockchain technology is used to store the encrypted image along with the hash value of the original medical image to accomplish integrity and traceability. At the cloud server side, the disease diagnosis process takes place in different stages, namely, multilevel thresholding-based segmentation, MobileNet-based feature extraction, and optimal kernel extreme learning machine (OKELM)-based classification. Furthermore, a multiobjective political optimizer is designed for effective selection of threshold values and KELM parameters. A wide range of simulations was performed on two benchmark medical image datasets, and the experimentation results highlighted the promising performance of the BESITD technique over the recent techniques with the maximum accuracy of 0.9816.

Optimized video internet of things using elliptic curve cryptography based encryption and decryption

The adaptive Joint Photographic Experts Group 2000 (JPEG2000) image compression approach employing the wavelet image transform had proposed with the rise of the optimized Video Internet of Things (VIoT) using image transmission security using Elliptic Curve Cryptography (ECC) techniques. Compressive Sensing (CS) for periodic data transfers has shown to be an excellent option for Wireless Sensor Networks (WSNs) since CS-based sensor communications drastically reduce data transmissions and enhance energy efficiency. However, another issue that arises when utilizing the optimized VIoT with image transmission compression is data loss as a result of various security risks during transmission. The numerous cooperative communication strategies proved their feasibility in different ways. However, additional issues must be handled while handling image transmission in VIoT. For example, when preparing to transmit an image, you may expend a lot of energy. The main objective of this paper is to increase image quality while minimizing processing time and error rates. However, force aptitude is vital to the research problem for the Wireless Multimedia Sensor Network (WMSN), as high-dimensional digital images use greater processing capabilities of sensor nodes. The image in WMSN is transmitted through a large number of relays. The experimental findings demonstrate the effectiveness of the suggested paradigm.