Method for hiding text data in an image

2021 ◽  
Vol 83 (1) ◽  
pp. 72-79
Author(s):  
O.A. Kan ◽  
◽  
N.A. Mazhenov ◽  
K.B. Kopbalina ◽  
G.B. Turebaeva ◽  
...  
Keyword(s):  
Secret Message ◽  
Source Image ◽  
Hidden Information ◽  
Text Data ◽  
Code Table ◽  
Ascii Code ◽  
Text Information ◽  
Graphic File ◽  
Image Pixels ◽  
Digital Fingerprints

The main problem: The article deals with the issues of hiding text information in a graphic file. A formula for hiding text information in image pixels is proposed. A steganography scheme for embedding secret text in random image pixels has been developed. Random bytes are pre-embedded in each row of pixels in the source image. As a result of the operations performed, a key image is obtained. The text codes are embedded in random bytes of pixels of a given RGB channel. To form a secret message, the characters of the ASCII code table are used. Demo encryption and decryption programs have been developed in the Python 3.5.2 programming language. A graphic file is used as the decryption key. Purpose: To develop an algorithm for embedding text information in random pixels of an image. Methods: Among the methods of hiding information in graphic images, the LSB method of hiding information is widely used, in which the lower bits in the image bytes responsible for color encoding are replaced by the bits of the secret message. Analysis of methods of hiding information in graphic files and modeling of algorithms showed an increase in the level of protection of hidden information from detection. Results and their significance: Using the proposed steganography scheme and the algorithm for embedding bytes of a secret message in a graphic file, protection against detection of hidden information is significantly increased. The advantage of this steganography scheme is that for decryption, a key image is used, in which random bytes are pre-embedded. In addition, the entire pixel bits of the container image are used to display the color shades. It can also be noted that the developed steganography scheme allows not only to transmit secret information, but also to add digital fingerprints or hidden tags to the image.

scholarly journals FEATURES OF HIDING INFORMATION IN IMAGES WITH USING THE LEAST SIGNIFICANT BIT

2020 ◽  
Vol 20 ◽  
pp. 17-22
Author(s):  
A. Lagun ◽  
O. Polotai
Keyword(s):  
Secret Message ◽  
Pseudorandom Sequence ◽  
Text File ◽  
Least Significant Bit ◽  
Hidden Information ◽  
Digital Text ◽  
Secret Information ◽  
Text Information ◽  
Image Pixels ◽  
Sequence Generator

In the article has considered the peculiarities of steganographic algorithms implemenation for hiding information in inmoveable images. Authors has described different embedding algorithms which use the method of least significant bit. In particular, the use of digital filtering allows you to better select the necessary pixels for embedding, and the use of a pseudorandom sequence generator allows you to more effectively hide secret information, complicating the search for secret information to the attacker.From the existing color palettes to represent inmoveable images have been selected the most common RGB pal-ette, which contains red, green, and blue intensities to produce image pixels. Colors that are less sensitive to the human eye are used to form the filled steganographic containers to provide additional visual stability.Also, in the paper authors have investigated the features of hiding digital text information in a inmoveable image as a BMP file and have realized an algorithm that for images of different size allows you to hide a text file of the necessary size. In particular, the number of bytes of the secret message is written to the original container to retrieve the required number of characters during searching. In addition, it takes into account the peculiarities of forming a BMP file that contains additional alignment bytes of the string.In general, the algorithm allows you to select a container file of the appropriate size to hide the secret information, as well as the colors of the palette in which the information will be embedded. The extracting of secret information occurs until the number of bytes of the hidden message is reached. This value has recorded at the beginning of the hiding text. You can use encryption or compression algorithms to complication searching of clear text by attacker. Only users those who are aware of the algorithms used and perhaps the keys will be able to read the hidden information correctly.

scholarly journals Aspect-level sentiment analysis merged with knowledge graph and graph convolutional neural network

2021 ◽  
Vol 2083 (4) ◽  
pp. 042044
Author(s):  
Zuhua Dai ◽  
Yuanyuan Liu ◽  
Shilong Di ◽  
Qi Fan
Keyword(s):  
Neural Network ◽  
Sentiment Analysis ◽  
Structural Information ◽  
Knowledge Graph ◽  
Convolutional Network ◽  
Text Data ◽  
Short Text ◽  
Fine Grained ◽  
Syntactic Information ◽  
Text Information

Abstract Aspect level sentiment analysis belongs to fine-grained sentiment analysis, w hich has caused extensive research in academic circles in recent years. For this task, th e recurrent neural network (RNN) model is usually used for feature extraction, but the model cannot effectively obtain the structural information of the text. Recent studies h ave begun to use the graph convolutional network (GCN) to model the syntactic depen dency tree of the text to solve this problem. For short text data, the text information is not enough to accurately determine the emotional polarity of the aspect words, and the knowledge graph is not effectively used as external knowledge that can enrich the sem antic information. In order to solve the above problems, this paper proposes a graph co nvolutional neural network (GCN) model that can process syntactic information, know ledge graphs and text semantic information. The model works on the “syntax-knowled ge” graph to extract syntactic information and common sense information at the same t ime. Compared with the latest model, the model in this paper can effectively improve t he accuracy of aspect-level sentiment classification on two datasets.

Dual Scaling in Data Mining from Text Databases

2006 ◽  
Vol 10 (4) ◽  
pp. 451-457 ◽  
Author(s):  
Junzo Watada ◽  
◽  
Keisuke Aoki ◽  
Masahiro Kawano ◽  
Muhammad Suzuri Hitam ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Text Mining ◽  
Kansei Engineering ◽  
Semantic Meaning ◽  
Dual Scaling ◽  
Text Documents ◽  
Text Data ◽  
Text Document ◽  
Text Information ◽  
Quantification Model

The availability of multimedia text document information has disseminated text mining among researchers. Text documents, integrate numerical and linguistic data, making text mining interesting and challenging. We propose text mining based on a fuzzy quantification model and fuzzy thesaurus. In text mining, we focus on: 1) Sentences included in Japanese text that are broken down into words. 2) Fuzzy thesaurus for finding words matching keywords in text. 3) Fuzzy multivariate analysis to analyze semantic meaning in predefined case studies. We use a fuzzy thesaurus to translate words using Chinese and Japanese characters into keywords. This speeds up processing without requiring a dictionary to separate words. Fuzzy multivariate analysis is used to analyze such processed data and to extract latent mutual related structures in text data, i.e., to extract otherwise obscured knowledge. We apply dual scaling to mining library and Web page text information, and propose integrating the result in Kansei engineering for possible application in sales, marketing, and production.

scholarly journals USAGE OF THE STEGANOGRAPHIC ALGORITHMS FOR TEXT INFORMATION HIDING

2018 ◽  
pp. 49-56
Author(s):  
A. Lagun
Keyword(s):  
Secret Message ◽  
Binary Representation ◽  
Text File ◽  
Left Edge ◽  
Amount Of Information ◽  
Variable Quantity ◽  
Cryptographic Keys ◽  
System User ◽  
Text Information ◽  
Replacement Algorithms

Today cryptographic and steganographic systems provide the best information security of society. Cryptography transforms information into the incomprehensible form with using the cryptographic keys and algorithms. Steganography hides the secret information in unknown place of object. The steganographic algorithms, which hide message in text container, are researched in the article. For process of hiding are used the text file-container properties. The hide message converts to the binary numbers system. User puts ones or zeros into the defined places of text file-container. These places have special characteristics. There may be two types of hiding: insertion and replacement. In case of insertion the hiding message adds to file-container with using invisible characters in viewing mode of text file. Then the size of full container with hided message is bigger than size of empty container. If used the replacing method then the characters of file-container replace to other characters that are almost the same as the first ones. For example, anyone is possible replacement of characters that have the same appearance in different languages. In this case the sizes of the empty and filled container remain the same. One of the simplest hiding methods is insertion the variable quantity of the space characters between words of text file. Suppose, that zero of hidden message is coded by one space character and one - is coded by two space characters. Therefore, depending on hidden message one or two space characters are located in different places of the text. Also, the author considers another hiding type, which uses the same view of some characters of different languages. If you look at the characters in Ukrainian and English, than the 18 characters in the each language is the same – 'a','c','e','i','o','p','x','A','B','C','E','H','I','K','O','P','T','X'. When hiding for the values of zeros in hidden message the file-container remains the same, and for the values of ones in hidden message the characters of language file-container replace to the same characters of another language (Ukrainian-English).The results of the algorithm work show us, that when using characters from different languages in the hiding process, the full file-container is much smaller than when encoding the space characters. The last algorithm which is considered in work uses tail space characters. It forms a filled container with enlarged text strings depending on the number of space characters which the hidden message determines. One character of hidden message is written in two file-container text strings. In particular the binary representation of each character is divided into two parts with four bits, and at the end of each text string is written no more than 15 space characters. The number of space characters corresponds to the decimal value of each part. To ensure hiding of secret message full container has the form aligned to the left edge of the text. Considered algorithms of hiding message in text container are used for the confidential information defense. Algorithms, which use insertion of invisible characters, allow hiding the amount of information that corresponds to the number of space characters with certain characteristics. The most of replacement algorithms hide more information than insertion algorithms. Also replacement algorithms do not change file-container size. For example, algorithm, which replace characters of different alphabets, hides such amount of information, which depends on the statistics of used languages. The most problem of using text containers is providing its steganographic defense. In particular, if user enables the unprintable character view in a text editor, then could see the some statistic of location invisible symbols added by the insertion methods. Therefore decoding of hidden message is simplified. The hidden message with using replacement algorithms is more defensible, but using of compression algorithms to the full container deletes the hidden information.

scholarly journals Classification and pattern extraction of incidents: a deep learning-based approach

2022 ◽  
Author(s):  
Sobhan Sarkar ◽  
Sammangi Vinay ◽  
Chawki Djeddi ◽  
J. Maiti
Keyword(s):  
Superior Performance ◽  
Hidden Information ◽  
Text Data ◽  
Moment Estimation ◽  
The Arts ◽  
Proposed Model ◽  
Benchmark Datasets ◽  
Unexplored Area ◽  
Key Terms

AbstractClassifying or predicting occupational incidents using both structured and unstructured (text) data are an unexplored area of research. Unstructured texts, i.e., incident narratives are often unutilized or underutilized. Besides the explicit information, there exist a large amount of hidden information present in a dataset, which cannot be explored by the traditional machine learning (ML) algorithms. There is a scarcity of studies that reveal the use of deep neural networks (DNNs) in the domain of incident prediction, and its parameter optimization for achieving better prediction power. To address these issues, initially, key terms are extracted from the unstructured texts using LDA-based topic modeling. Then, these key terms are added with the predictor categories to form the feature vector, which is further processed for noise reduction and fed to the adaptive moment estimation (ADAM)-based DNN (i.e., ADNN) for classification, as ADAM is superior to GD, SGD, and RMSProp. To evaluate the effectiveness of our proposed method, a comparative study has been conducted using some state-of-the-arts on five benchmark datasets. Moreover, a case study of an integrated steel plant in India has been demonstrated for the validation of the proposed model. Experimental results reveal that ADNN produces superior performance than others in terms of accuracy. Therefore, the present study offers a robust methodological guide that enables us to handle the issues of unstructured data and hidden information for developing a predictive model.

Text Information Extraction: in Context of Indian Languages

2012 ◽  
Vol 433-440 ◽  
pp. 5012-5019
Author(s):  
Ganesh K. Sethi ◽  
Rajesh K. Bawa
Keyword(s):  
Information Extraction ◽  
Foreign Languages ◽  
Indian Languages ◽  
Text Data ◽  
The Past ◽  
Lighting Conditions ◽  
Video Frames ◽  
Text Information

Text data present in images contains useful information and its extraction involves detection, localization, extraction, enhancement and recognition. However, the problem is challenging due to fact that text can have various styles, size, orientations, alignments, effect of lighting conditions. While a large number of techniques have been proposed in the past for extracting text from images and video frames for foreign languages, not much research has been carried out for Indian languages. The purpose of this paper is to review various algorithms for the problem for foreign as well as for the for the Indian languages.

scholarly journals A review of arabic text steganography: past and present

2020 ◽  
Vol 17 (2) ◽  
pp. 1040
Author(s):  
Suhaibah Jusoh ◽  
Aida Mustapha ◽  
Azizan Ismail ◽  
Roshidi Din
Keyword(s):  
Comparative Analysis ◽  
English Language ◽  
Arabic Language ◽  
Secret Message ◽  
Arabic Text ◽  
Hidden Information ◽  
Secret Information ◽  
Text Steganography

<span>Steganography is a strategy for hiding secret information in a cover document in order to avoid attacker from predict about hidden information. Steganography exploit cover message, for instance text, audio, picture and video to hide the secret message. Before this, linguistic text steganographic techniques are implemented just for the English language. But nowadays different languages are used to hide the data like Arabic language. This language is still new in the steganography and still need practices for empowerment. This paper will present the text steganographic method for Arabic language, scholar paper within 5 year will be analyze and compared. The main objective of this paper is to give the comparative analysis in the Arabic steganography method that has been applied by previous researchers. Finally, the disadvantage and advantage of the method also will be presented in this paper.</span>

scholarly journals Penggunaan Karakter Kontrol ASCII Untuk Integrasi Data Pada Hasil Enkripsi Algoritma Caesar Cipher

2021 ◽  
Vol 6 (3) ◽  
pp. 197
Author(s):  
Nur Putrananda Setyapuji Winarno ◽  
Triawan Adi Cahyanto
Keyword(s):  
Success Rate ◽  
New Method ◽  
Complex Solution ◽  
Text Data ◽  
Simple Method ◽  
Standard Code ◽  
Ascii Code ◽  
American Standard ◽  
Control Character ◽  
Information Interchange

Cryptography is a technique or method for securing data from other unauthorized parties. The substitution algorithm is the simplest algorithm and is classified as a classic in the field of cryptography, for example the Caesar cipher algorithm. ASCII code (American Standard Code for Information Interchange) is a code that contains characters that can be processed by a computer. By type, not all ASCII characters can be printed by the computer. Some characters are not printed or illegible as usual. These unreadable characters are called control characters. The control character can be used to improve the performance of the Caesar Cipher algorithm, because it focuses on processing text data. The application of control characters uses a simple method but has a complex solution. The results of this study are in the form of a new method with the Caesar cipher algorithm as a classical cryptographic method or technique and ASCII characters as the basis for the development of the ciphertext performance resulting from the encryption process. In testing this method, the success rate reaches 100% in securing the contents of the document with a sample of 500 letters. While the possibility of solving the ciphertext results is classified as difficult because the control characters of the ciphertext results that are illegible will make the decryption result multiple interpretations.

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