Arindrajit Seal

Bio: Arindrajit Seal is an academic researcher from University of North Carolina at Charlotte. The author has contributed to research in topics: Cloud computing & Encryption. The author has an hindex of 5, co-authored 8 publications receiving 105 citations. Previous affiliations of Arindrajit Seal include Kalyani Government Engineering College.

A Novel Lossless Image Encryption Method using DNA Substitution and Chaotic Logistic Map

Abstract: Presently, there is a growth in the transmission of image and video data. Security becomes a main issue. Very strong image cryptographic techniques are a solution to this problem. There is a use of a randomly generated public key and based on that there is an application of DNA algorithm. In the proposed method DNA algorithm based substitution is used for spatial domain bit permutation. Here the chaotic logistic map is used for generating a pseudorandom bit sequence. We have generated 48bit length sequences for every pixel. After the substitution operation, a final layer of security is imposed to make this process more fault tolerant. The For checking the strength of the work a series of tests are performed and various parameters are checked like Correlation Coefficient Analysis, analysis of NPCR and UACI values etc.

An Efficient Image Cryptographic Algorithm based on Frequency Domain using Haar Wavelet Transform

Abstract: Presently a number of techniques are used to restrict confidential image data from unauthorized access. In this paper, the authors have proposed an efficient lossless image cryptographic algorithm to transmit pictorial data securely. Initially we take a 64 bit key, we convert our decimal pixel value into binary 8 bits and we XOR the first 8 bits of the key with the pixel value. After that we take the next 8 bits of the key and XOR with the next pixel value. We perform the circular right shit operation when the key gets exhausted. We perform the first level haar wavelet decomposition thereafter. Dividing the LL1 into four equal sections we perform some swapping operations. Decryption follows the reverse of the encryption .Evaluation is done by some parametric tests which includes correlation analysis, NPCR, UACI readings etc. show that the proposed work is resilient and robust in the field of cryptography.

A New and Resilient Image Encryption Technique Based on Pixel Manipulation, Value Transformation and Visual Transformation Utilizing Single–Level Haar Wavelet Transform

Abstract: Lossless image cryptography is always preferred over lossy image cryptography. In this approach the authors have proposed a very resilient and novel image encryption/decryption algorithm. Initially the image is first converted to frequency components and the encryption is performed on sub-bands and the encrypting algorithm is found to be very strong, reliable and strong. The encryption algorithm involves pixel breakup into two parts and reversing parts of the pixel. The results show a deviation of pixel between the images present in the original and encrypted domains. The decryption algorithm is exactly the encryption algorithm in reverse. The proposed algorithm is evaluated by standard measures and it is seen to be attack-resistant to well-known attacks.

On the Emerging Coexistence of Edge, Fog and Cloud Computing paradigms in Real-Time Internets-of-EveryThings which operate in the Big-Squared Data space

Abstract: This report motivates the roles of Cloud computing, Edge computing, and the hierarchically distributed cooperative Fog computing, for the real-time analysis of big-data in Internets-of-Everything (IoEs). IoEs are enhanced Internets of Things (IoTs) which integrate people, process, data and heterogeneous “Things”: compute, storage, and sensor/actuator hardware. The ubiquitousness of IoE devices, the ever-increasing amount of big data in IoEs, and the need for real-time computing in IoEs have motivated the problem of distributed data storage and analysis. With trillions (big-data scale) of IoE devices on the verge of being deployed in tomorrow's ever-connected and autonomous society, and with the expected big-data generated by each such IoE device (typically image data of the order of tens and hundreds of gigabytes per day), we are rapidly approaching Big-Squared data dimensions. The power consumption of traditional cloud data centers are already about 70% of all power generated, and it will increase exponentially if Cloud computing is the only solution for tomorrow's IoEs. Moreover, the big-squared data from merging IoEs will create network and compute level bottlenecks that will be impractical from a realtime standpoint, especially in case of rapid mobility in IoEs. Hence, the need for distributed hierarchical Fog computing and associated data management. We survey key features of emerging IoEs, the existing big-data computing and storage frameworks, and point out their capabilities and deficiencies. Finally, we discuss the design and implementation of our Fog computing architecture.

Fog Computing for Real-Time Accident Identification and Related Congestion Control

Abstract: This paper focuses on developing (i) a benchmark application for Real-Time traffic incidence identification and related traffic management, using Real-Time congestion-aware navigation of smart vehicles (Edge nodes) with video feeds, (ii) an image database for Deep Learning used for recognition and classification of traffic incidences such as accidents and congestions, (iii) the System Level Software (or Middleware) required for Distributed Computing in such a heterogeneous Real-Time constrained system with Rapid Mobility - today’s Internet-of-Everything (IoE), and (iv) a hardware prototype of the distributed computing and storage infrastructure. The video bandwidth requirement of 10-100 GigaBytes of data per minute per vehicular camera makes it a Big Data problem. With millions of smart vehicles projected to be deployed within the next 5 years, BigData from a single vehicle, multiplied with the large number of vehicles, presents a Big-Squared-Data computing space which will easily overwhelm any Cloud infrastructure with its Real-Time or near Real-Time demands. Hence the need for a Fog tier between the Edge nodes and the Cloud to bring distributed computation (servers) and storage closer to the Edge nodes. Such a Fog consists of multiple Fog instances, each one of which services cells or Virtual Clusters of Edge nodes. Results show that Fog-Cloud computing framework outperforms a Cloud-only platform by 79.7% reduction in total latency or response time.

Medical Image Encryption Based on Hybrid Chaotic DNA Diffusion

Abstract: We explore the use of two chaotic systems (Bernoulli shift map and Zizag map) coupled with deoxyribonucleic acid coding in an encryption scheme for medical images in this paper. The scheme consists of two main phases: Chaotic key generation and DNA diffusion. Firstly, the message digest algorithm 5 hash function is performed on the plain medical image and the hash value used in combination with the value of an input ASCII string to generate initial conditions and control parameters for two chaotic systems (Bernoulli shift map and Zigzag map). These chaotic systems are subsequently used to produce two separate key matrices. Secondly, a row-by-row diffusion operation between the plain image matrix and the two chaotic key matrices, using the DNA XOR algebraic operation is performed in an alternating pattern to produce the cipher image. The logistic map is used to select the DNA encoding and decoding rules for each row. Experimental results of statistical, differential and key analyses demonstrate that the proposed scheme is robust and provides resistance to various forms of attacks.

An image encryption approach using particle swarm optimization and chaotic map

Abstract: Encryption is predominantly crucial in order to provide safeguard to sensitive data, specifically images, against any possible illegitimate access and transgressions. This paper presents to propose an optimized image encryption approach for secure image-based communication. The approach makes use of particle swarm optimization to receive optimized encryption effect and a chaotic map. Initially, the approach generates several encrypted images and chaotic Logistic map, where session key for map’s initial conditions are made dependent on pending plain-image. Subsequently, the encrypted images are served as particles and an initial assemblage to operate optimization through PSO. The optimized encrypted image is manifested by correlation coefficient relevant to contiguous pixels as fitness function. The simulation results of proposed encryption approach demonstrate that encrypted image exhibits high de-correlation of adjacent pixels along with other excellent encryption lineaments such as flat histograms, entropies, net pixel change rates, and unified average changing intensities.

Internet of Spatial Things: A New Reference Model With Insight Analysis

Abstract: The Internet of Things (IoT) is the concept of everyday objects that make us live in the era of the IoT. The spatial characteristics of things around us can be considered the reins of the IoT operability. In other words, the spatial variation of a thing can be the core of the IoT reaction. For example, the spatial variation in crop indicates the requirement and characteristic of agriculture production. Also, the spatial variation of a human movement can alarm the security and monitoring systems. This issue agitates the contemplating of the “Internet of Spatial Things (IoST)” concept. For the first time, this paper draws an inspiration towards the perspective vision of the IoST, which is concerned with revise IoT with the spatial perspective. The IoST concept is argued by the presentation of its definition and architectural components. Besides, the IoST layers are discussed in details. Furthermore, a new proposed reference model of the IoST is proposed. Finally, the new trends and open issues regarding the IoST are aroused.

A new algorithm for the construction of substitution box by using chaotic map

Abstract: In the last few decades, security of digital information has become more important. This study focuses on the substitution of grayscale digital images. By using Cubic-Logistic map, a new substitution box is proposed which has been given the name Cubic-Logistic substitution box. Some substituted images have been examined which are substituted with the proposed and other existing substitution boxes. There is hardly any information in the substituted image which is substituted with the proposed substitution box, whereas patterns of the original image can be visualized in those substituted image which are substituted with other existing substitution boxes. Cubic-Logistic substitution box has also shown good statistical analysis such as strict avalanche criterion, differential approximation probability, linear approximation probability, nonlinearity, bit independent criterion, correlation and energy. These analyses of proposed Cubic-Logistic substitution box are then compared with the analysis of other existing substitution boxes such as Rijndael substitution box, Affine-Power-Affine substitution box and skipjack substitution box to show the strength of the proposed S-box.

Biomedical image enhancement based on modified Cuckoo Search and morphology

Abstract: This work describes an method for biomedical image enhancement using modified Cuckoo Search Algorithm with some Morphological Operation. In recent years, various digital image processing techniques are developed. Computer Vision, machine interfaces, manufacturing industry, data compression for storage, vehicle tracking and many more are some of the domains of digital image processing application. In most of the cases, digital biomedical images contains various types of noise, artifacts etc. and are not useful for direct applications. Before using it in any process, the input image has to be gone through some preprocessing stages; such preprocessing is generally called as image enhancement. In this work, a new technique has been proposed to enhance biomedical images using modified cuckoo search algorithm and morphological operation. Presence of noise and other unwanted objects generates distortion in an image and it will affect the ultimate result of the process. In case of biomedical images, accuracy of the results is very important. It may also decrease the discernibility of many features inside the images. It can affect the classification accuracy. In this work, this issue has been targeted and improved by obtaining better contrast value after converting the color image into grayscale image. The basic property of the cuckoo search algorithm is that the amplitudes of its components are capable to objectively describe the contribution of the gray levels to the formation of image information for the best contrast value of a digital image. The proposed method modified the conventional cuckoo search method by employing the McCulloch's method for levy flight generation. After computing the best contrast value, morphological operation has been applied. In morphological operation based phase, the intensity parameters are tuned for quality enhancement. Experimental results illustrate the effectiveness of this work.