• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제14권3호
• /
• pp.1228-1248
• /
• 2020

Steganalysis & steganography have witnessed immense progress over the past few years by the advancement of deep convolutional neural networks (DCNN). In this paper, we analyzed current research states from the latest image steganography and steganalysis frameworks based on deep learning. Our objective is to provide for future researchers the work being done on deep learning-based image steganography & steganalysis and highlights the strengths and weakness of existing up-to-date techniques. The result of this study opens new approaches for upcoming research and may serve as source of hypothesis for further significant research on deep learning-based image steganography and steganalysis. Finally, technical challenges of current methods and several promising directions on deep learning steganography and steganalysis are suggested to illustrate how these challenges can be transferred into prolific future research avenues.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제9권12호
• /
• pp.5209-5228
• /
• 2015

According to the characteristics of adaptive JPEG steganography which determines the changed DCT coefficients based on embedding distortion, a new steganalysis method by selecting the DCT coefficients with small distortion values is proposed. Firstly, the principle of adaptive JPEG steganography through minimizing distortion is introduced. Secondly, the practicability of selecting the changed DCT coefficients according to distortion values is studied. Thirdly, the proposed steganalysis method is given and the embedding sensitivity of the steganalysis feature extracted from the selected DCT coefficients is analyzed. Lastly, the implement processes of the proposed method are presented and analyzed in details. In the experiments, PQt, PQe and J-UNIWARD steganography are used as examples to verify the effect of the proposed steganalysis method for adaptive JPEG steganography. A serial experimental results show the detection accuracy can be improved obviously, especially when the payload is relatively low.

• IEIE Transactions on Smart Processing and Computing
• /
• 제5권4호
• /
• pp.250-255
• /
• 2016

Steganography is the art and science of secure communication. It focuses on both security and camouflage. Steganographic techniques must produce the resultant stego-image with less distortion and high resistance to steganalysis attack. This paper is mainly concerned with two steganographic techniques-least significant bit (LSB)++ and the reversible histogram transformation function (RHTF). LSB++ is likely to produce less distortion in the output image to avoid suspicion, but it is vulnerable to steganalysis attacks. RHTF using a mod function technique is capable of resisting the most popular and efficient steganalysis attacks, such as the regular-singular pair attack and chi-squared detection steganalysis, but it produces a lot of distortion in the output image. In this paper, we propose a new steganographic technique by combining both methods. The experimental results show that the proposed technique overcomes the respective drawbacks of each method.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제11권1호
• /
• pp.552-569
• /
• 2017

The existing steganalysis method based on 2D Gabor filters can achieve a competitive detection performance for content-adaptive JPEG steganography. However, the feature dimensionality is still high and the time-consuming of feature extraction is relatively large because the optimal selection is not performed for 2D Gabor filters. To solve this problem, a new steganalysis method is proposed for content-adaptive JPEG steganography by selecting the optimal 2D Gabor filters. For the proposed method, the 2D Gabor filters with different parameter settings are generated first. Then, the feature is extracted by each 2D Gabor filter and the corresponding detection accuracy is used as the measure for filter selection. Next, some 2D Gabor filters are selected by a greedy strategy and the steganalysis feature is extracted by the selected filters. Last, the ensemble classifier is used to assemble the proposed steganalysis feature as well as the final steganalyzer. The experimental results show that the steganalysis feature extracted by the selected optimal 2D Gabor filters also can achieve a competitive detection performance while the feature dimensionality is reduced greatly.

• 정보과학회 컴퓨팅의 실제 논문지
• /
• 제23권4호
• /
• pp.244-249
• /
• 2017

스테그아날리시스(Steganalysis)란 이미지 등 일반적인 자료에 암호화된 정보를 은닉하는 스테가노그래피(Steganography)에 대한 검출 및 분석 방법으로, 기계학습 기반 방법론을 포함한다. 기존 기계학습 기반 스테그아날리시스는 영상(Image)의 특징(Feature) 추출 및 모델링에 기반하며, 최근 딥러닝(Deep Learning)의 적용으로 검출 정확도가 큰 폭으로 향상되었다. 하지만 현존하는 스테그아날리시스 모델은 단일 스테가노그래피 기법에 대해 국한되어 있어 학습에 사용되지 않은 스테고(Stego) 이미지의 경우 검출이 불가능한 결정적 한계를 가진다. 본 연구에서는 다양한 스테가노그래피 기법으로 생성된 스테고 이미지에 딥러닝을 적용하여 스테그아날리시스를 학습하는 범용적 모델을 제안한다. 다양한 실험을 통해 제안 기법의 효용성 및 가능성을 확인하고, 범용적 스테그아날리시스 모델이 각각에 특화된 검출 기법과 유사한 정확도로 스테고 이미지를 검출할 수 있음을 보인다.

• 전자공학회논문지
• /
• 제50권6호
• /
• pp.221-227
• /
• 2013

본 논문에서는 영상의 범주화에 근거한 개선된 스테그분석 방법을 제안한다. 대부분의 스테그분석 방법은 영상이 가지는 고유한 특성과는 무관하게 영상의 전역적 특징을 나타내는 통계적 모멘트에 기반하여 특징 벡터를 추출한다. 그러나 모멘트에 근거한 방법은 서로 다른 복잡도의 영상에 사용됨으로써 스테그분석의 성능 저하를 야기시키게 된다. 본 논문에서는 8비트 영상을 상위 4 비트 및 하위 4 비트 평면으로 분해하고, 이들 간의 상관계수에 따라 영상을 두 클래스로 범주화한다. 이와같이 범주화된 영상들은 각각에 대하여 독립적으로 스테그분석을 시행할 수 있다. 본 논문의 방법은 영상의 범주에 따라 독립적으로 스테그분석을 수행함으로써 통계적 모멘트를 사용한 방법이 가지는 단점을 완화할 수 있다. 제안된 스테그분석 방법의 성능을 평가하기 위해 기존의 잘 알려진 네 가지 스테그분석 방법과 비교하였으며, 실험 결과 기존의 방법에 비해 더 높은 검출율을 보임을 확인할 수 있었다.

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

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제6권8호
• /
• pp.1926-1945
• /
• 2012

In this paper, two practical forensics aided steganalyzers (FA-steganalyzer) for heterogeneous bitmap images are constructed, which can properly handle steganalysis problems for mixed image sources consisting of raw uncompressed images and JPEG decompressed images with different quality factors. The first FA-steganalyzer consists of a JPEG decompressed image identifier followed by two corresponding steganalyzers, one of which is used to deal with uncompressed images and the other is used for mixed JPEG decompressed images with different quality factors. In the second FA-steganalyzer scheme, we further estimate the quality factors for JPEG decompressed images, and then steganalyzers trained on the corresponding quality factors are used. Extensive experimental results show that the proposed two FA-steganalyzers outperform the existing steganalyzer that is trained on a mixed dataset. Additionally, in our proposed FA-steganalyzer scheme, we can select the steganalysis methods specially designed for raw uncompressed images and JPEG decompressed images respectively, which can achieve much more reliable detection accuracy than adopting the identical steganalysis method regardless of the type of cover source.

• 방송공학회논문지
• /
• 제24권2호
• /
• pp.315-328
• /
• 2019

영상 스테그아날리시스는 입력 영상을 스테가노그래피 알고리즘이 적용된 스테고 영상과 스테가노그래피 알고리즘이 적용되지 않은 커버 영상으로 분류하는 알고리즘이다. 기존에는 주로 수제 특징 기반의 스테그아날리시스를 연구하였다. 하지만 CNN 기반의 물체 인식이 큰 성과를 이루면서 최근 CNN 기반의 스테그아날리시스가 활발히 연구되고 있다. CNN 기반의 스테그아날리시스는 물체 인식과는 달리 커버 영상과 스테고 영상의 미세한 차이를 식별하기 위해서 전처리 필터를 필요로 한다. 그러므로, CNN 기반의 스테그아날리시스 연구들은 효과적인 전처리 필터와 네트워크 구조를 개발하는 데 초점을 두고 있다. 본 논문에서는 동일한 실험 조건에서 기존 연구들을 비교하고, 그 결과를 기반으로 전처리 필터와 네트워크 구조적인 차이에 의한 성능 변화를 분석한다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제10권12호
• /
• pp.5643-5656
• /
• 2016

The detection accuracy of steganalysis depends on many factors, including the embedding algorithm, the payload size, the steganalysis feature space and the properties of the cover source. In practice, the cover source mismatch (CSM) problem has been recognized as the single most important factor negatively affecting the performance. To address this problem, we propose a new framework for blind, universal steganalysis which uses traditional steganalyst features. Firstly, cover images with the same statistical properties are searched from a reference image database as aided samples. The test image and its aided samples form a whole test set. Then, by assuming that most of the aided samples are innocent, we conduct outlier detection on the test set to judge the test image as cover or stego. In this way, the framework has removed the need for training. Hence, it does not suffer from cover source mismatch. Because it performs anomaly detection rather than classification, this method is totally unsupervised. The results in our study show that this framework works superior than one-class support vector machine and the outlier detector without considering the image retrieval process.