scholarly journals Hiding a Covert Digital Image by Assembling the RSA Encryption Method and the Binary Encoding Method

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Kuang Tsan Lin ◽  
Sheng Lih Yeh
Keyword(s):  
Digital Image ◽  
Binary Data ◽  
Holographic Image ◽  
Binary Encoding ◽  
Image Dimension ◽  
Encoding Method ◽  
Encryption Method

The Rivest-Shamir-Adleman (RSA) encryption method and the binary encoding method are assembled to form a hybrid hiding method to hide a covert digital image into a dot-matrix holographic image. First, the RSA encryption method is used to transform the covert image to form a RSA encryption data string. Then, all the elements of the RSA encryption data string are transferred into binary data. Finally, the binary data are encoded into the dot-matrix holographic image. The pixels of the dot-matrix holographic image contain seven groups of codes used for reconstructing the covert image. The seven groups of codes are identification codes, covert-image dimension codes, covert-image graylevel codes, pre-RSA bit number codes, RSA key codes, post-RSA bit number codes, and information codes. The reconstructed covert image derived from the dot-matrix holographic image and the original covert image are exactly the same.

Reducing the effect of crosstalk noise from defocused multi-depth holographic image with a rasterize encoding method

2019 ◽  
pp. 955
Author(s):  
Siao-Ting Li ◽  
Chih-Hao Chuang ◽  
Chung Feng Kuo ◽  
Hoang-Yan Lin ◽  
Chin-I Huang ◽  
...  
Keyword(s):  
Crosstalk Noise ◽  
Holographic Image ◽  
Encoding Method

Bond Graph Binary Encoding Method for Genetic Algorithms Applications

2003 ◽  
Author(s):  
Germa´n L. Di´az-Cuevas ◽  
Roger F. Ngwompo
Keyword(s):  
Genetic Algorithms ◽  
Dynamic Systems ◽  
Binary Code ◽  
Worked Examples ◽  
Bond Graphs ◽  
Binary Encoding ◽  
Coding Method ◽  
Performance Specifications ◽  
Model Equations ◽  
Encoding Method

A binary encoding method for bond graphs that can be used for genetic algorithms (GAs) applications is presented. The originality of the proposed coding is that it encompasses causal information. This ensures that causal analysis is taken into account in assessing the fitness of topologies generated in GA operations and the suitability of design candidates to meet performance specifications can be tested directly from the binary code as the model equations can be derived from it. The code is suitable for GAs applications on bond graphs (BG) for topology and parameter optimisation in automated synthesis of dynamic systems. The coding method and its possible applications are illustrated through worked examples.

Every bit counts: The binary representation of typed data and programs

2012 ◽  
Vol 22 (4-5) ◽  
pp. 529-573 ◽  
Author(s):  
ANDREW J. KENNEDY ◽  
DIMITRIOS VYTINIOTIS
Keyword(s):  
Binary Data ◽  
General Framework ◽  
Binary Representation ◽  
Proof Assistant ◽  
A Value ◽  
The Coq Proof Assistant ◽  
Binary Encoding ◽  
Coq Proof Assistant ◽  
Definition Of ◽  
Formal Properties

AbstractWe show how the binary encoding and decoding of typed data and typed programs can be understood, programmed and verified with the help of question–answer games. The encoding of a value is determined by the yes/no answers to a sequence of questions about that value; conversely, decoding is the interpretation of binary data as answers to the same question scheme. We introduce a general framework for writing and verifying game-based codecs. We present games in Haskell for structured, recursive, polymorphic and indexed types, building up to a representation of well-typed terms in the simply-typed λ-calculus with polymorphic constants. The framework makes novel use of isomorphisms between types in the definition of games. The definition of isomorphisms together with additional simple properties make it easy to prove that codecs derived from games never encode two distinct values using the same code, never decode two codes to the same value and interpret any bit sequence as a valid code for a value or as a prefix of a valid code. Formal properties of the framework have been proved using the Coq proof assistant.

A commutative digital image watermarking and encryption method in the tree structured Haar transform domain

2011 ◽  
Vol 26 (1) ◽  
pp. 1-12 ◽  
Author(s):  
M. Cancellaro ◽  
F. Battisti ◽  
M. Carli ◽  
G. Boato ◽  
F.G.B. De Natale ◽  
...  
Keyword(s):  
Digital Image ◽  
Image Watermarking ◽  
Digital Image Watermarking ◽  
Transform Domain ◽  
Haar Transform ◽  
Encryption Method

Data-In-Image: a novel concept for data manipulation and encryption implementation

2021 ◽  
Author(s):  
Mohamed Abdelwahed
Keyword(s):  
Machine Learning ◽  
Binary Data ◽  
Numerical Data ◽  
Digital Data ◽  
Response Code ◽  
Quick Response Code ◽  
Data Files ◽  
Novel Concept ◽  
Encryption Method ◽  
Image Format

<p>Conventionally, the way of storing and exchange numerical data depends mainly on binary data files in compressible form. In this era of the Big Data and machine learning systems and with the accumulation of data with different forms and types, it is important to find an alternative way for handling the data. The binary data are software dependent which does not exhibit its content and type without accessing the data by the proper software. In addition, it does not have any encryption ability. To solve this issue, we propose a new concept to handle the digital data in a descriptive, encrypted, compressed form, and able to be previewed. The idea is to pack the binary bits into a bitmap image with specific coding scheme. This approach employs the Steim scheme as a primary compression tool with a 128-bit encryption method then packs the encrypted codes into a WebP image file. The WebP image is featured by being an independent, web friendly, and highly compressed file. In order to make the file describing its contents, we reserved some pixels as coded descriptive pixels. By this way, the now packed data exhibits its contents and type during image preview.</p><p>It is proven that the Data-In-Image format, regardless of being encrypted, occupies the least amount of storage space among other image formats that can be easily handled, stored, and shared through clouds and devices safely with a lower cost. For seismic data,  the size of the WebP image comprises ~20% of the corresponding binary size with a bit-rate of ~5.6 b/s which is smaller than that of the Steim form, 27% and 8.9 b/s, respectively. Regarding the compression speed, it is found that the code compresses data with a rate of ~11,118 samples/s or ~ 44 Kbytes/s in average.</p><p>In addition, the data image is able to be digitally scanned and with some modifications can be remotely accessed like the quick response code, the thing that is not possible in the binary form. Moreover, the descriptive pixels in the image allow the implementations of smart tools to archive and classify data by machine learning and recognition algorithms.</p>

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