• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권11호
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• pp.5592-5609
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• 2017

JPEG steganography detection is an active research topic in the field of information hiding due to the wide use of JPEG image in social network, image-sharing websites, and Internet communication, etc. In this paper, a new steganalysis method for content-adaptive JPEG steganography is proposed by integrating the evolutionary feature selection and classifier ensemble selection. First, the whole framework of the proposed steganalysis method is presented and then the characteristic of the proposed method is analyzed. Second, the feature selection method based on genetic algorithm is given and the implement process is described in detail. Third, the method of classifier ensemble selection is proposed based on Pareto evolutionary optimization. The experimental results indicate the proposed steganalysis method can achieve a competitive detection performance by compared with the state-of-the-art steganalysis methods when used for the detection of the latest content-adaptive JPEG steganography algorithms.

• Journal of the Optical Society of Korea
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• 제12권3호
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• pp.136-146
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• 2008

We proposed an adaptive steganography of an optical image using bit-planes and multichannel characteristics. The experiment's purpose was to compare the most popular methods used in optical steganography and to examine their advantages and disadvantages. In this paper we describe two digital methods: the first uses less significant bits(LSB) to encode hidden data, and in the other all blocks of $n{\times}n$ pixels are coded by using DCT(Digital Cosine Transformation), and two optical methods: double phase encoding and digital hologram watermarking with double binary phase encoding by using IFTA(Iterative Fourier Transform Algorithm) with phase quantization. Therefore, we investigated the complexity on bit plane and data, similarity insert information into bit planes. As a result, the proposed method increased the insertion capacity and improved the optical image quality as compared to fixing threshold and variable length method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권6호
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• pp.1674-1688
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• 2023

The remarkable advancement of quantum steganography offers enhanced security for quantum communications. However, there is a significant concern regarding the potential misuse of this technology. Moreover, the current research on identifying malicious quantum steganography is insufficient. To address this gap in steganalysis research, this paper proposes a specialized quantum steganalysis algorithm. This algorithm utilizes quantum machine learning techniques to detect steganography in general quantum secure communication schemes that are based on pure states. The algorithm presented in this paper consists of two main steps: data preprocessing and automatic discrimination. The data preprocessing step involves extracting and amplifying abnormal signals, followed by the automatic detection of suspicious quantum carriers through training on steganographic and non-steganographic data. The numerical results demonstrate that a larger disparity between the probability distributions of steganographic and non-steganographic data leads to a higher steganographic detection indicator, making the presence of steganography easier to detect. By selecting an appropriate threshold value, the steganography detection rate can exceed 90%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권6호
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• pp.1619-1637
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• 2024

In today's highly digitized landscape, securing digital communication is paramount due to threats like hacking, unauthorized data access, and network policy violations. The response to these challenges has been the development of cryptography applications, though many existing techniques face issues of complexity, efficiency, and limitations. Notably, sophisticated intruders can easily discern encrypted data during transmission, casting doubt on overall security. In contrast to encryption, steganography offers the unique advantage of concealing data without easy detection, although it, too, grapples with challenges. The primary hurdles in image steganography revolve around the quality and payload capacity of the cover image, which are persistently compromised. This article introduces a pioneering approach that integrates image steganography and encryption, presenting the BitPatternStego method. This novel technique addresses prevalent issues in image steganography, such as stego-image quality and payload, by concealing secret data within image pixels with identical bit patterns as their characters. Consequently, concerns regarding the quality and payload capacity of steganographic images become obsolete. Moreover, the BitPatternStego method boasts the capability to generate millions of keys for the same secret message, offering a robust and versatile solution to the evolving landscape of digital security challenges.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권1호
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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.

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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• 제9권4호
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• pp.470-485
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• 2003

멀티미디어 데이타중에서 오디오데이타를 이용한 상용화된 오디오 스테가노그라피(audio steganography) 소프트웨어들은 시각적인 측면에서 비밀 메시지가 은닉되어 있다는 것을 쉽게 인지할 수 있다는 것과 숨길 정보의 크기에 제한이 있다는 문제점을 가지고 있다. 이러한 문제점을 해결하기 위해 동적으로 메시지를 은닉하는 방법을 제안하였다. 또한 비밀 메시지의 보안수준을 향상시키기 위해 파일 암호화 알고리즘도 적용하였다. 본 논문에서는 제안한 오디오 스테가노그라피를 수행해주는 StegoWaveK시스템을 상용화된 오디오 스테가노그라피 시스템의 5가지 측면으로 비교 분석하였으며, 성능면에서 우수함을 보였다. StegoWaveK는 상용화된 시스템에 비해 시각적 공격 측면이나 은닉할 메시지 크기 측면에서는 좋으나 인터페이스 측면에서는 사용자 위주의 편리성을 제공할 수 있도록 보완되어야 한다. 그리고 StegoWavek와 상용화된 시스템이 견고성이 약하다는 단점을 보완해야 하며, 다양한 멀티미디어 데이타를 이용한 스테가노그라피로의 추후연구가 필요하다.

• 인터넷정보학회논문지
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• 제22권6호
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• pp.151-162
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• 2021

스테가노그래피 기술은 은닉하고자 하는 정보를 미세한 노이즈의 형태로 변환하여 이미지, 비디오, 오디오 같은 멀티미디어 파일 속에 은닉함으로써 정보은닉 여부를 육안으로 알 수 없게 하는 것이 주 목적인 고도의 은닉기법으로, 각종 간첩 행위 및 사이버 공격에 악용되어 왔다. SNS 메신저는 스테가노그래피의 주요 매개체로 활용되는 멀티미디어 파일을 송수신하는데 있어서 다양한 장점을 가진 매력적인 플랫폼이다. 본 연구에서는 SNS 메신저 환경에서 썸네일 이미지의 활용을 통해 완전한 수신율을 보장하는 두 가지의 새로운 스테가노그래피 통신 기법을 제안했다. 또한, 대표적인 SNS 메신저인 카카오톡을 사용한 실제 환경에서 제안한 기법들에 대한 구현과 시험을 통해 실현 가능성을 검증하였다. 본 연구를 통해 새로운 유형의 스테가노그래피 통신 기법을 제안함으로써 스테가노그래피 기술의 위험성을 재평가하고, 상응하는 방어기법에 대한 후속 연구의 촉발을 도모한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권10호
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• pp.2514-2526
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• 2013

This paper presents a detection method for least significant bit matching revisited (LSBMR) steganography. Previous research shows that the adjacent pixels of natural images are highly correlated and the value 0 appears most frequently in pixel difference. Considering that the message embedding process of LSBMR steganography has a weighted-smoothing effect on the distribution of pixel difference, the frequency of the occurrence of value 0 in pixel difference changes most significantly whereas other values approximately remain unchanged during message embedding. By analyzing the effect of LSBMR steganography on pixel difference distribution, an equation is deduced to estimate the frequency of difference value 0 using the frequencies of difference values 1 and 2. The sum of the ratio of the estimated value to the actual value as well as the ratio of the frequency of difference value 1 to difference value 0 is used as the steganalytic detector. Experimental results show that the proposed method can effectively detect LSBMR steganography and can outperform previous proposed methods.

• 한국산업정보학회논문지
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• 제22권5호
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• pp.9-15
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• 2017

디지털 시대에 인터넷에서 사용되는 모든 데이터는 디지털화되어 통신 네트워크를 통해 송신 및 수신된다. 따라서 디지털 데이터가 불법적인 사용자에 의해 변조되고 조작될 수 있기 때문에 기밀성과 무결성을 갖춘 암호화된 데이터를 전송하는 것이 중요하다. 스테가노그래피는 암호화 기법과 혼합되어 기밀성과 무결성을 함께 보장하기 위한 효율적인 방법이다. 커버 매체에 삽입되는 위치와 변화하는 어절 형태를 기반으로 비밀 메시지를 삽입하는 한글 텍스트 스테가노그래피 방법을 제안한다. 한글 텍스트 스테가노그래피에서 3.35%의 삽입용량과 0.4%의 파일 크기 변화를 고려할 때 실험결과는 Jaro_score 값이 0.946으로 유지할 필요가 있다는 것을 보여준다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권11호
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• pp.4153-4169
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• 2014

To recognize F5-like (such as F5 and nsF5) steganographic algorithm from multi-class stego images, a recognition algorithm based on the identifiable statistical feature (IDSF) of F5-like steganography is proposed in this paper. First, this paper analyzes the special modification ways of F5-like steganography to image data, as well as the special changes of statistical properties of image data caused by the modifications. And then, by constructing appropriate feature extraction sources, the IDSF of F5-like steganography distinguished from others is extracted. Lastly, based on the extracted IDSFs and combined with the training of SVM (Support Vector Machine) classifier, a recognition algorithm is presented to recognize F5-like stego images from images set consisting of a large number of multi-class stego images. A series of experimental results based on the detection of five types of typical JPEG steganography (namely F5, nsF5, JSteg, Steghide and Outguess) indicate that, the proposed algorithm can distinguish F5-like stego images reliably from multi-class stego images generated by the steganography mentioned above. Furthermore, even if the types of some detected stego images are unknown, the proposed algorithm can still recognize F5-like stego images correctly with high accuracy.