• Proceedings of the IEEK Conference
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• 2003.11b
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• pp.23-26
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• 2003

Westfeld[1] analyzed a sequential LSB embedding steganography effectively through the $\chi$$^2$-statistical test which measures the frequencies of PoVs(pairs of values). Fridrich also proposed another statistical analysis, so-called RS steganalysis by which the embedding message rate can be estimated. In this paper, we propose a new steganographic scheme which preserves the above two statistics. The proposed scheme embeds the secret message in the innocent image by randomly adding one to real pixel value or subtracting one from it, then adjusts the statistical measures to equal those of the original image.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.1
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• pp.366-381
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• 2020

Steganography has been successfully employed in various applications, e.g., copyright control of materials, smart identity cards, video error correction during transmission, etc. Deep learning-based steganography models can hide information adaptively through network learning, and they draw much more attention. However, the capacity, security, and robustness of the existing deep learning-based steganography models are still not fully satisfactory. In this paper, three models for different cases, i.e., a basic model, a secure model, a secure and robust model, have been proposed for different cases. In the basic model, the functions of high-capacity secret information hiding and extraction have been realized through an encoding network and a decoding network respectively. The high-capacity steganography is implemented by hiding a secret image into a carrier image having the same resolution with the help of concat operations, InceptionBlock and convolutional layers. Moreover, the secret image is hidden into the channel B of carrier image only to resolve the problem of color distortion. In the secure model, to enhance the security of the basic model, a steganalysis network has been added into the basic model to form an adversarial network. In the secure and robust model, an attack network has been inserted into the secure model to improve its robustness further. The experimental results have demonstrated that the proposed secure model and the secure and robust model have an overall better performance than some existing high-capacity deep learning-based steganography models. The secure model performs best in invisibility and security. The secure and robust model is the most robust against some attacks.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.14 no.4
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• pp.39-47
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• 2004

In general, the steganographic algorithm for embedding message in JPEG images, such as Jsteg$^{(1)}$ , JP Hide ＆ Seek$^{(2)}$ , F5$^{(3)}$ , outGuess$^{(4)}$ replaces the LSB of DCT coefficients by the message bits. Both Jsteg and n Hide ＆ seek are detected by $\chi$$^2$- test, steganalytic technique$^{(4)}$ , the rate of detection is very low, though. In this Paper, we Propose a new steganalysis method that determine not only the existence of hidden messages in JPEG images exactly, but also the steganographic algorithm used. This method is advanced from the technique Blockiness$^{(5)}$ . It has many advantages that include a computational efficiency, correctness and that can detect without bowing steganographic algorithm. Experiment results show the superiority of our approach over Blockiness$^{(5)}$ .

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.4
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• pp.217-222
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• 2022

Information plays an important role in modern society. Most of the information is processed and moved in the digital space. In cyberspace, confidential communication based on resistance and security is fundamental. It is essential to protect the information sent and received over the network. However, information may be leaked and forged by unauthorized users. The effectiveness of the existing protection system decreases as an innovative technique is applied to identify the communication contents by a third party. Steganography is a technique for inserting secret information into a specific area of a medium. Stegganography and steganalysis techniques are at odds with each other. A new and sophisticatedly implemented system is needed to cope with the advanced steganalysis. To enhance step-by-step diffusion and irregularity, I propose a hybrid implementation technique of image steganography for Hangul messages based on layered encryption and variable ShiftRows. PSNR was calculated to measure the proposed steganography efficiency and performance. Compared to the basic LSB technique, it was shown that the diffusion and randomness can be increased even though the PSNR decreased by 1.45%.

• Journal of Korea Society of Industrial Information Systems
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• v.20 no.3
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• pp.29-36
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• 2015

Steganography is the technique of hiding the existence of a secret message that is communicated hiddenly. Generally, the main objectives of this paper is to develop newer and more sophisticated steganographic techniques based on perceptual transparency, robustness and capacity of the hidden data. This paper analyzes the advantages and disadvantages of image steganography techniques and proposes an effective method. As a result, the images steganography technique based on good ELSB and DCT which applies the rearranged key is secure and effective.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.1024-1026
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• 2017

인트라 블록과 인터 블록의 상관계수를 이용하여 이미지의 특징을 뽑아내고, 이를 SVM에 학습시켜 원본과 스테고 영상을 판별한다. 스테고 영상은 F3 알고리즘을 개선한 F4알고리즘을 직접 구현하여 만들어냈다. 실험에 사용한 데이터는 SIPI, BOSS, 자체 수집 데이터베이스에서 학습용 영상 120장, 테스트용 영상 500장을 이용하였다. 원본 500장에 대해 2장이 F4로 판별 되었고, F4 500장에 대해서는 전부 F4로 판별하여 99.8%의 정확도를 달성하였다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.06a
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• pp.249-251
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• 2019

본 논문에서는 CNN 기반 스테그아날리스 방법을 이용하여 입력 영상에 비밀 메시지가 삽입되었는지를 판별하고, 비밀 메시지가 삽입되었을 경우 WOW 와 UNIWARD 방법 중에 어떤 방법으로 삽입되었는지를 분류하고자 한다. 이를 위해 입력 영상으로부터 특징 정보를 추출하기 위해 사용되는 전처리(prepropcessing) 필터의 수가 분류 성능에 미치는 영향에 대해 분석한다. SRM 필터를 사용한 실험에서 필터의 수를 단순히 증가시키는 것은 성능 향상이 도움이 되지 않으며, 효과적인 필터를 선별해서 사용하는 것이 보다 우수한 성능을 가짐을 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.4
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• pp.1553-1567
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• 2021

Steganography is a current hot research topic in the area of information security and privacy protection. However, most previous steganography methods are not effective against steganalysis and attacks because they are usually carried out by modifying covers. In this paper, we propose an improved coverless text steganography algorithm based on pretreatment and Part of Speech (POS), in which, Chinese character components are used as the locating marks, then the POS is used to hide the number of keywords, the retrieval of stego-texts is optimized by pretreatment finally. The experiment is verified that our algorithm performs well in terms of embedding capacity, the embedding success rate, and extracting accuracy, with appropriate lengths of locating marks and the large scale of the text database.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.986-1005
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• 2022

Text steganography is one of the most imminent and promising research interests in the information security field. With the unprecedented success of the neural network and natural language processing (NLP), the last years have seen a surge of research on generative linguistic steganography (GLS). This paper provides a thorough and comprehensive review to summarize the existing key contributions, and creates a novel taxonomy for GLS according to NLP techniques and steganographic encoding algorithm, then summarizes the characteristics of generative linguistic steganographic methods properly to analyze the relationship and difference between each type of them. Meanwhile, this paper also comprehensively introduces and analyzes several evaluation metrics to evaluate the performance of GLS from diverse perspective. Finally, this paper concludes the future research work, which is more conducive to the follow-up research and innovation of researchers.

• Journal of Korea Society of Industrial Information Systems
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• v.14 no.3
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• pp.37-43
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• 2009

Steganography conceals the fact that hidden message is being sent on the internet. Steganalysis can be detected the abrupt changes in the statistics of a stego-data. After message embedding, I have analyzed for the statistical significance of the fact the occurrence of differences among the four-neighboring pixels. In this case, when a embedding messages within a images is small, use EC value and chi-square test to determine whether a distribution in an images matches a distribution that shows distortion from stego-data.