Joint image compression–encryption scheme using entropy coding and compressive sensing

Singh

Mathematical Problems in Engineering

2021

Images contain very sensitive and confidential information. Because images play a significant role in many applications such as military communication, remote-sensing, and medical-imaging, therefore, it is necessary to protect sensitive and confidential information from unauthorized use and modification. To achieve this objective, encryption is one of the best methods among the information hiding methods. In recent years, many image encryption approaches are designed by the researchers. They use different concepts for image encryption to increase security. The main aim of this paper is to present a comprehensive review of the existing image encryption approaches. These approaches are categorized based on different concepts such as chaotic maps, DNA, compressive sensing, and optical image encryption. Comparisons are made among the existing approaches to access the various security parameters. Key performance metrics are also presented. The future scope of image encryption is also presented to encourage the research community.

Section: Compressive Sensing-based Image Encryptionmentioning

confidence: 99%

Computational Image Encryption Techniques: A Comprehensive Review

Singh

Mathematical Problems in Engineering

2021

International Journal of Distributed Sensor Networks

“…In Figure 6, D represents the vacated spaces used for embedding additional data. To realize data extraction and image recovery, some auxiliary information as a part of payload should be embedded into the cipher image, which includes: (1) classes c in interval 1,32 with six bits by letter p for representation, (2) cluster tag of each pixel in original image is marked L , (3) 6-bit delimiter “0 0 0 1 1 1” used to differentiate two cipher categories C 1 ′ , C 2 ′ and each encrypted class in C 2 ′ , (4) q bits of classes belonging to categories C 1. Thus, the embedding rate r could be calculated by equation (24)…”

Section: Proposed Schemementioning

confidence: 99%

Privacy-preserved data hiding using compressive sensing and fuzzy C-means clustering

Wang

Fan

2020

Nowadays, digital images are confronted with notable privacy and security issues, and many research works have been accomplished to countermeasure these risks. In this article, a novel scheme for data hiding in encrypted domain is proposed using fuzzy C-means clustering and compressive sensing technologies to protect privacy of the host image. The original image is preprocessed first to generate multiple highly correlated classes with fuzzy C-means clustering algorithm. Then, all classes are further divided into two parts according to proper threshold. One is encrypted by stream cipher, and the other is encrypted and compressed simultaneously with compressive sensing technology for easy data embedding by information hider. The receiver can extract additional data and recover the original image with data-hiding key and encryption key. Experiments and analysis demonstrate that the proposed scheme can achieve higher embedding rate about additional data and better visual quality of recovered image than other state-of-the-art schemes.

ICST Transactions on Scalable Information Systems

“…Where 6R means 6 count of red pixels, 3G means 3 count of green pixels, 4B means 4 count of blue pixels and so on. The technique encodes only the consecutive number of same color pixels i.e., the probability of occurrence of consecutive same color is high [46,48].…”

Section: Rrrrrrgggbbbbrrrrrrrrbbbbbgggggrrrrrrrmentioning

confidence: 99%

“…Speed of compression depends on compression technique adopted and the results of the parameter is dependent upon the size of memory. Lossy compression techniques [46] increases the computational complexity and storage. It is measured as ratio of compressed output file size per unit time required for compressing the file in seconds [24and is expressed mathematically in eq.…”

Section: Compression Speedmentioning

confidence: 99%

Performance analysis of compression algorithms for information security: A Review

Sharma

Batra

2018

Data compression is a vital part of information security, since compressed data is much more secure and convenient to handle. Effective data compression technique creates an effective, secure, easy communicable & redundant data. There are two types of compression algorithmic techniques:-lossy and lossless. These techniques can be applied to any data format like text, audio, video or image file. The primary objective of this study was to analyse data compression techniques used for information security techniques like steganography, cryptography etc. Four each, lossy and lossless techniques are implemented and evaluated on parameters like-file size, saving percentage, time, compression ratio and speed. Detailed analysis shows that the lossy techniques performs better quantitatively whereas lossless is better qualitatively. However, lossless techniques are more effective as there is no data loss during the process. Among all, Huffman encoding outperforms other algorithms.