• ETRI Journal
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• v.34 no.3
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• pp.429-438
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• 2012

This study presents two reversible data hiding schemes based on the coefficient shifting (CS) algorithm. The first scheme uses the CS algorithm with a mean predictor in the spatial domain to provide a large payload while minimizing distortion. To guard against manipulations, the second scheme uses a robust version of the CS algorithm with feature embedding implemented in the integer wavelet transform domain. Simulations demonstrate that both the payload and peak signal-to-noise ratio generated by the CS algorithm with a mean predictor are better than those generated by existing techniques. In addition, the marked images generated by the variant of the CS algorithm are robust to various manipulations created by JPEG2000 compression, JPEG compression, noise additions, (edge) sharpening, low-pass filtering, bit truncation, brightness, contrast, (color) quantization, winding, zigzag and poster edge distortion, and inversion.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.9
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• pp.3286-3301
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• 2014

Jung and Yoo proposed the first image interpolation-based reversible data hiding algorithm. Although their algorithm achieved superior interpolation results, the embedding capacity was insufficient. Lee and Huang proposed an improved algorithm to enhance the embedding capacity and the interpolation results. However, these algorithms present limitations to magnify the original image to any resolution and pixels in the boundary region of the magnified image are poorly manipulated. Furthermore, the capacity and the image quality can be improved further. This study modifies the pixel mapping scheme and adopts a bilinear interpolation to solve boundary artifacts. The modified reference pixel determination and an optimal pixel adjustment process can effectively enhance the embedding capacity and the image quality. The experimental results show our proposed algorithm achieves a higher embedding capacity under acceptable visual distortions, and can be applied to a magnified image at any resolution. Our proposed technique is feasible in reversible data hiding.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.4
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• pp.1248-1266
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• 2012

This paper presents a multilevel reversible data hiding method based on histogram shifting which can recover the original image losslessly after the hidden data has been extracted from the stego-image. The method of prediction is adopted in our proposed scheme and prediction errors are produced to explore the similarity of neighboring pixels. In this article, we propose two different predictors to generate the prediction errors, where the prediction is carried out using the center prediction method and the JPEG-LS median edge predictor (MED) to exploit the correlation among the neighboring pixels. Instead of the original image, these prediction errors are used to hide the secret information. Moreover, we also present an improved method to search for peak and zero pairs and also talk about the analogy of the same to improve the histogram shifting method for huge embedding capacity and high peak signal-to-noise ratio (PSNR). In the one-level hiding, our method keeps image qualities larger than 53 dB and the ratio of embedding capacity has 0.43 bpp (bit per pixel). Besides, the concept with multiple layer embedding procedure is applied for obtaining high capacity, and the performance is demonstrated in the experimental results. From our experimental results and analytical reasoning, it shows that the proposed scheme has higher PSNR and high data embedding capacity than that of other reversible data hiding methods presented in the literature.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.407-408
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• 2006

In this paper, we propose a new reversible image authentication technique based on watermarking where if the image is authentic, the distortion due to embedding can be completely removed from the watermarked image after the hidden data has been extracted. This technique utilizes histogram characteristics of the difference image and modifies pixel values slightly to embed more data than other reversible data hiding algorithm. The proposed scheme is quite simple and the execution time is rather short. Experimental results demonstrate that the proposed scheme can detect any modifications of the watermarked image.

• ETRI Journal
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• v.37 no.5
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• pp.990-1000
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• 2015

This paper presents an overlapping pattern substitution (PS) method. The original overlapping PS method as a reversible data hiding scheme works well with only four pattern pairs among fifteen possible such pairs. This paper generalizes the original PS method so that it will work well with an optimal pair from among the fifteen possible pattern pairs. To implement such an overlapping PS method, changeable and embeddable patterns are first defined. A class map is virtually constructed to identify the changeable and embeddable pairs. The run-lengths between consecutive least probable patterns are recorded. Experiments show that an implementation of our overlapping PS method works well with any possible type of pairs. Comparison results show that the proposed method achieves more embedding capacity, a higher PSNR value, and less human visual distortion for a given embedding payload.

• ETRI Journal
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• v.38 no.1
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• pp.159-163
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• 2016

Block compressed sensing (BCS) is widely used in image sampling and is an efficient, effective technique. Through the use of BCS, an image can be simultaneously compressed and encrypted. In this paper, a novel reversible data hiding (RDH) method is proposed to embed additional data into BCS images. The proposed method is the first RDH method of its kind for BCS images. Results demonstrate that our approach performs better compared with other state-of-the-art RDH methods on encrypted images.

• Proceedings of the Korean Information Science Society Conference
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• 2000.10b
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• pp.90-92
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• 2000

데이터마이닝 문제는 데이터를 그 속성들에 따라 분류하여 예측하는 것뿐만 아니라 분류된 속성들간의 연관성에 대해 잘 설명할 수 있어야 한다. 일반적으로 변수들간의 연관성을 잘 설명할 수 있으면서도 높은 예측력을 가지는 방법으로는 베이지안 네트웍 분류자(Bayesian network classifier)가 있다. 그러나 이것은 데이터 마이닝과 같은 대용량 데이터에서는 성능이 떨어지는 단점이 있다. 이에 이 논문에서는 최근 RBF 신경망이 입력변수 선정문제에 성공적으로 적용된 Reversible Jump Markov Chain Monte Carlo 방법을 이용하여 최적의 입력변수들만을 선택하여 베이지안 네트웍을 학습하는 Selective BN Augmented Naive-Bayes Classifier를 새로운 방안으로 제안하고 이를 실제 데이터마이닝 문제에 적용한 결과를 제시한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.619-634
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• 2019

In the field of traceability systems, researchers focus on applications in the agricultural food traceability and scanning commodities. The purposes of this paper, however, is to propose an efficient and reliable traceability system that can be applied to all kinds of commodities. Currently, most traceability systems store data in a central server, which is unreliable when the system is under attack or if the administrator tampers with the data for personal interests. Therefore, it is necessary to design a system that can eliminate these threats. In this paper, we propose a decentralized and non-reversible traceability system for storing commodity data. This system depends on blockchain technology, which organizes data in the form of chains without a central server. This chain-style storage mechanism can prevent malicious modifications. In addition, some strategies are adopted to reduce the storage pressure and response time when the system has stored all kinds of commodity data.

• 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.1
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• pp.132-148
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• 2013

Via the Internet, the information infrastructure of modern health care has already established medical information systems to share electronic health records among patients and health care providers. Data hiding plays an important role to protect medical images. Because modern medical devices have improved, high resolutions of medical images are provided to detect early diseases. The high quality medical images are used to recognize complicated anatomical structures such as soft tissues, muscles, and internal organs to support diagnosis of diseases. For instance, 16-bit depth medical images will provide 65,536 discrete levels to show more details of anatomical structures. In general, the feature of low utilization rate of intensity in 16-bit depth will be utilized to handle overflow/underflow problem. Nowadays, most of data hiding algorithms are still experimenting on 8-bit depth medical images. We proposed a novel reversible data hiding scheme testing on 16-bit depth CT and MRI medical image. And the peak point and zero point of a histogram are applied to embed secret message k bits without salt-and-pepper.