• Journal of Korea Multimedia Society
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• v.24 no.6
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• pp.768-788
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• 2021

The reversible data hiding technique safely transmits secret data to the recipient from malicious attacks by third parties. In addition, this technique can completely restore the image used as a transmission medium for secret data. The reversible data hiding schemes have been proposed in various forms, and recently, the reversible data hiding schemes based on interpolation are actively researching. The reversible data hiding scheme based on the interpolation method expands the original image into the cover image and embed secret data. However, the existing interpolation-based reversible data hiding schemes did not embed secret data during the interpolation process. To improve this problem, this paper proposes embedding the first secret data during the image interpolation process and embedding the second secret data into the interpolated cover image. In the embedding process, the original image is divided into blocks without duplicates, and the maximum and minimum values are determined within each block. Three way searching based on the maximum value and two way searching based on the minimum value are performed. And, image interpolation is performed while embedding the first secret data using the PVD scheme. A stego image is created by embedding the second secret data using the maximum difference value and log function in the interpolated cover image. As a result, the proposed scheme embeds secret data twice. In particular, it is possible to embed secret data even during the interpolation process of an image that did not previously embed secret data. Experimental results show that the proposed scheme can transmit more secret data to the receiver while maintaining the image quality similar to other interpolation-based reversible data hiding schemes.

• Knowledge Management Research
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• v.21 no.1
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• pp.99-116
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• 2020

Text is the most widely used means of exchanging or expressing knowledge and information in the real world. Recently, researches on structuring unstructured text data for text analysis have been actively performed. One of the most representative document embedding method (i.e. doc2Vec) generates a single vector for each document using the whole corpus included in the document. This causes a limitation that the document vector is affected by not only core words but also other miscellaneous words. Additionally, the traditional document embedding algorithms map each document into only one vector. Therefore, it is not easy to represent a complex document with interdisciplinary subjects into a single vector properly by the traditional approach. In this paper, we introduce a multi-vector document embedding method to overcome these limitations of the traditional document embedding methods. After introducing the previous study on multi-vector document embedding, we visually analyze the effects of the multi-vector document embedding method. Firstly, the new method vectorizes the document using only predefined keywords instead of the entire words. Secondly, the new method decomposes various subjects included in the document and generates multiple vectors for each document. The experiments for about three thousands of academic papers revealed that the single vector-based traditional approach cannot properly map complex documents because of interference among subjects in each vector. With the multi-vector based method, we ascertained that the information and knowledge in complex documents can be represented more accurately by eliminating the interference among subjects.

• Journal of Korea Multimedia Society
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• v.15 no.7
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• pp.891-902
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• 2012

Reversible data hiding scheme is a form of steganography in which the secret embedding data can be retrieved from a stego image for the purpose of identification, copyright protection and making a covert channel. The reversible data hiding should satisfy that not only are the distortions due to artifacts against the cover image invisible but also it has large embedding capacity as far as possible. In this paper, we propose a robust reversible data hiding scheme by exploiting the differences between a center pixel and its neighboring pixels in each sub-block of the image to embed secret data into extra space. Moreover, our scheme enhances the embedding capacity and can recover the embedded data from the stego image without causing any perceptible distortions to the cover image. Simulation results show that our proposed scheme has lower visible distortions in the stego image and provides robustness to geometrical image manipulations, such as rotation and cropping operations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.3
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• pp.600-612
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• 2013

In 2006, Zhang and Wang proposed a data hiding scheme based on Exploiting Modification Direction (EMD) to increase data hiding capacity. The major benefit of EMD is providing embedding capacity greater than 1 bit per pixel. Since then, many EMD-type data hiding schemes have been proposed. However, a serious disadvantage common to these approaches is that the embedded data is compromised when the embedding function is disclosed. Our proposed secure data hiding scheme remedies this disclosure shortcoming by employing an additional modulus function. The provided security analysis of our scheme demonstrates that attackers cannot get the secret information from the stegoimage even if the embedding function is made public. Furthermore, our proposed scheme also gives a simple solution to the overflow/underflow problem and maintains high embedding capacity and good stegoimage quality.

• Journal of the Korean Society of Safety
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• v.36 no.5
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• pp.61-70
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• 2021

This study aimed to analyze the research trends of the abstract data of ergonomic studies registered in MEDLINE, a medical bibliographic database, using word embedding. Medical-related ergonomic studies mainly focus on work-related musculoskeletal disorders, and there are no studies on the analysis of words as data using natural language processing techniques, such as word embedding. In this study, the abstract data of ergonomic studies were extracted with a program written with selenium and BeutifulSoup modules using python. The word embedding of the abstract data was performed using the word2vec model, after which the data found in the abstract were vectorized. The vectorized data were visualized in two dimensions using t-Distributed Stochastic Neighbor Embedding (t-SNE). The word "ergonomics" and ten of the most frequently used words in the abstract were selected as keywords. The results revealed that the most frequently used words in the abstract of ergonomics studies include "use", "work", and "task". In addition, the t-SNE technique revealed that words, such as "workplace", "design", and "engineering," exhibited the highest relevance to ergonomics. The keywords observed in the abstract of ergonomic studies using t-SNE were classified into four groups. Ergonomics studies registered with MEDLINE have investigated the risk factors associated with workers performing an operation or task using tools, and in this study, ergonomics studies were identified by the relationship between keywords using word embedding. The results of this study will provide useful and diverse insights on future research direction on ergonomic studies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.2
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• pp.945-958
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• 2017

This paper proposes a novel method of reversible data hiding in JPEG images. After analyzing the JPEG features, we provide a new algorithm of selecting appropriate blocks and coefficients to carry secret messages. Instead of embedding data into the histogram of all coefficients, we propose a strategy of ordered embedding to hide data by histogram shifts in several rounds. On the recipient end, secret messages can be exactly extracted, and the original JPEG image can be losslessly recovered. Experimental results show that high embedding rate can be achieved with limited distortions. Most importantly, the marked JPEG file generated by the proposed method requires less storage size than state-of-the-art works.

• Korean Journal of Acupuncture
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• v.30 no.3
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• pp.151-160
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• 2013

Objectives : The purpose of this study is to review the thread embedding treatment studies of obesity in Traditional Chinese Medicine. Methods : We reviewed 59 studies on cat-gut-embedding treatment of obesity which were published from 2005 to 2012. The studies were searched using the data base-CAJ(China Academic Journals; 中國期刊全文數據庫)(2005~2012). Results : Selected 59 studies were divided into 27 case series and 32 control studies. The number of studies has increased since 2005, more rapidly in 2011~2012. Most case series reported were about effectiveness of thread embedding monotherapy treating obesity. Control studies reported were about effectiveness of thread embedding treatment compared with acupuncture, electroacupuncture, and medicine. Most studies on thread embedding treatment of obesity showed that its effectiveness was very good. Conclusions : There have been many reports of thread embedding treatment studies of obesity in Traditional Chinese Medicine. We believed that these studies could be applied to the clinical practice and basic research in Korea.

• Journal of KIISE:Computing Practices and Letters
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• v.9 no.1
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• pp.70-76
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• 2003

Digital watermarking is a technique embedding hidden information into multimedia data imperceptibly such as images and sounds. Generally an original image is transformed and coded watermark data is embedded in frequency domain watermarking models. In this paper, We propose a new data embedding method using Fresnel transform. A watermark image is fresnel-transformed and the intensity of transformed pattern is embedded into original image. Our watermarking model has the flexibility In data embedding. It is possible to get many embedding patterns from a single watermark image by using various distance parameters with Fresnel transform. All kinds of image models such af shape, letter and photo ran be used as a watermark data. The watermarking experiments were conducted to show the validity of the proposed method, and the results show that our method has the robustness against lossy compression, filtering and geometric transformation.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.11
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• pp.483-490
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• 2021

In order to learn an existing song and create a new song using an artificial neural network, it is necessary to convert the song into numerical data that the neural network can recognize as a preprocessing process, and one-hot encoding has been used until now. In this paper, we proposed a note embedding method using notes as a basic unit and a bar embedding method that uses the bar as the basic unit, and compared the performance with the existing one-hot encoding. The performance comparison was conducted based on quantitative evaluation to determine which method produced a song more similar to the song composed by the composer, and quantitative evaluation methods used in the field of natural language processing were used as the evaluation method. As a result of the evaluation, the song created with bar embedding was the best, followed by note embedding. This is significant in that the note embedding and bar embedding proposed in this paper create a song that is more similar to the song composed by the composer than the existing one-hot encoding.

• Journal of Electrical Engineering and Technology
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• v.11 no.4
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• pp.974-986
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• 2016

Reversible image watermarking, a type of digital data hiding, is capable of recovering the original image and extracting the hidden message with precision. A number of reversible algorithms have been proposed to achieve a high embedding capacity and a low distortion. While numerous algorithms for the achievement of a favorable performance regarding a small embedding capacity exist, the main goal of this paper is the achievement of a more favorable performance regarding a larger embedding capacity and a lower distortion. This paper therefore proposes a reversible data hiding algorithm for which a novel piecewise 2D auto-regression (P2AR) predictor that is based on a rhombus-embedding scheme is used. In addition, a minimum description length (MDL) approach is applied to remove the outlier pixels from a training set so that the effect of a multiple linear regression can be maximized. The experiment results demonstrate that the performance of the proposed method is superior to those of previous methods.