An Effective Data Embedding Technique Based on APPM in Transform Domain

This paper proposes an efficient data embedding technique based on adaptive pixel pair matching in transform domain. The basic principle of a Pixel Pair Matching (PPM) based data embedding technique is to use the values of a pixel pair as a reference coordinate and search a coordinate in the neighborhood set of that pixel pair according to given message digit. In order to conceal secret data the pixel pair is then replaced by the searched coordinate. In transform domain data embedding techniques, the image pixels are converted into transform domain by using a particular transform and then the secret data is embedded by using an efficient data embedding algorithm. In this paper the Haar transform is used. The proposed method not only offers lower embedding distortion but also more robust against various noise attacks. The experimental results shows that this method performs better when compared to the spatial domain technique

Effective Data Hiding Method Through Pixel Pair Matching

This work proposes a new data-hiding method based on pixel pair matching , which is to use the values of pixel pair as a reference coordinate, and find a coordinate in the neighborhood set of this pixel pair based on the given message. Further the pixel pair is replaced by the searched coordinate to cover the digit. Two methods has been proposed to overcome this problem one is Exploiting modification direction (EMD) and another is diamond encoding (DE). The proposed methods offer lower distortion as compared to the existing methods by providing more compact neighborhood sets and allowing embedded digits in any notational system

Introducing a New Evaluation Criteria for EMD-Base Steganography Method

Steganography is a technique to hide the presence of secret communication. When one of the communication elements is under the influence of the enemy, it can be used. The main measure to evaluate steganography methods in a certain capacity is security. Therefore, in a certain capacity, reducing the amount of changes in the cover media, creates a higher embedding efficiency and thus more security of an steganography method. Mostly, security and capacity are in conflict with each other, the increase of one lead to the decrease of the other. The presence of a single criterion that represents security and capacity at the same time be useful in comparing steganography methods. EMD and the relevant methods are a group of steganography techniques, which optimize the amount of changes resulting from embedding (security). The present paper is aimed to provide an evaluation criterion for this group of steganography methods. In this study, after a general review and comparison of EMD-based steganography techniques, we present a method to compare them exactly, from the perspective of embedding efficiency. First, a formula is presented to determine the value of embedding efficiency, which indicates the effect of one or more changes on one or more pixels. The results demonstrate that the proposed embedding efficiency formula shows the performance of the methods better when several changes are made on a pixel compared to the existing criteria. In the second step, we have obtained an upper bound, which determines the best efficiency for each certain capacity. Finally, based on the introduced bound, another evaluation criterion for a better comparison of the methods is presented

Optimization of medical image steganography using n-decomposition genetic algorithm

Protecting patients' confidential information is a critical concern in medical image steganography. The Least Significant Bits (LSB) technique has been widely used for secure communication. However, it is susceptible to imperceptibility and security risks due to the direct manipulation of pixels, and ASCII patterns present limitations. Consequently, sensitive medical information is subject to loss or alteration. Despite attempts to optimize LSB, these issues persist due to (1) the formulation of the optimization suffering from non-valid implicit constraints, causing inflexibility in reaching optimal embedding, (2) lacking convergence in the searching process, where the message length significantly affects the size of the solution space, and (3) issues of application customizability where different data require more flexibility in controlling the embedding process. To overcome these limitations, this study proposes a technique known as an n-decomposition genetic algorithm. This algorithm uses a variable-length search to identify the best location to embed the secret message by incorporating constraints to avoid local minimum traps. The methodology consists of five main phases: (1) initial investigation, (2) formulating an embedding scheme, (3) constructing a decomposition scheme, (4) integrating the schemes' design into the proposed technique, and (5) evaluating the proposed technique's performance based on parameters using medical datasets from kaggle.com. The proposed technique showed resistance to statistical analysis evaluated using Reversible Statistical (RS) analysis and histogram. It also demonstrated its superiority in imperceptibility and security measured by MSE and PSNR to Chest and Retina datasets (0.0557, 0.0550) and (60.6696, 60.7287), respectively. Still, compared to the results obtained by the proposed technique, the benchmark outperforms the Brain dataset due to the homogeneous nature of the images and the extensive black background. This research has contributed to genetic-based decomposition in medical image steganography and provides a technique that offers improved security without compromising efficiency and convergence. However, further validation is required to determine its effectiveness in real-world applications

A Study on Adaptive Lossless Data Hiding Schemes

由於科技的快速發展，經由網際網路傳遞的多媒體影音資訊與日俱增。然而，在網路所傳輸之資訊非常注重其安全度。近年來專家學者們陸續提出了新的研究成果，其中資訊隱藏技術，為資訊加密注入了新的契機，經由一張普遍使用的影像，不破壞影像原來的視覺品質，將額外的訊息或資料嵌入掩護影像中，達到機密通訊或版權保護的目的。由於嵌入過程一定會修改到原本的掩護媒體，造成一些永久性的破壞，而無失真資訊隱藏技術可以在取出訊息和資料後，將偽裝前的原始掩護影像完全無任何失真地還原回來。 本論文提出兩種具有可適應性的無失真資訊隱藏，相較於目前大部分的無失真資訊隱藏方法，每個像素的隱藏容量都是固定的，本論文所提出的第一個技術讓每個像素的隱藏容量是可變動的，根據我們的研究，差值擴充技術在邊緣地區會造成影像品質很嚴重的破壞，所以不適合藏入大量資料，而平滑地區可以被藏入較多的資訊量，因此，我們提出區塊式中央差值擴充技術來決定每一個區塊的隱藏量。另外，目前大部分以差值擴充技術為基礎的方法都需要記錄地圖資訊，而像素預測的方式可以解決此一額外資訊的浪費，所提出的第二個技術就是利用周圍像素的變異數來預測可隱藏容量，如此可同時降低失真程度與增加隱藏容量，偽裝後的影像品質與藏入的資訊量也可以達到一個較好的結果。實驗結果顯示，本研究所提的演算法在藏入資訊後，偽裝影像具有優異的視覺品質，而且還原後的影像具有無失真的特性，為多媒體資訊在網路傳輸上提供了安全而有效率的機制。 資料隱藏技術主要追求的目標為：資訊藏量要大，同時，偽裝隱蔽物的影像品質要好。然而，資訊藏量與偽裝隱蔽物影像品質是很難取捨的問題，因此本論文最後提出一個可根據藏入資料量大小，決定最佳影像品質的菱形編碼技術，如此可在資訊藏量與影像品質中取得一個最佳平衡。Due to the fast development of scientific technologies, cryptography has been widely used for information security. However, the using of cryptography is easy to be detected. Nowadays, the popular application of multi-media and transmission via network, the data hiding technologies have been widely applied for information protection, data monitoring and tracking, copyright protection, and source authentication. Traditional information/data hiding methods, which are ways to embed additional messages into host signals, have been applied to accomplish content protection and secret communication. The embedding process has to modify the original contents of cover signal without introducing perceptual changes, but some permanent destroy result from the modifications. Lossless data hiding scheme can have the cover image come back to its old self again without leaving any trace of distortion after the secret message or data extraction. Firstly, in this thesis, two novel lossless data hiding schemes have been proposed. Unlike the fixed hiding capacity each pixel provides in most of the currently available lossless data hiding approaches, the proposed first method utilizes a block-based lossless data embedding algorithm where the quantity of the hidden information each block bears is variable. Due to the fact that schemes with difference expansion tend to damage the image quality seriously in the edge areas, in the proposed schemes, smoother areas are chosen to conceal more secret bits. Therefore, we proposed a block-based centralized difference expansion technique to determine the capacity of each block. In addition, the location map is needed in most lossless data hiding schemes with difference expansion. Next, another lossless data hiding scheme with edge prediction and difference expansion without additional location map is proposed in the second scheme. This way, a better balance can be reached between the embedding ratio and the stego-image quality. Experimental results show that the proposed lossless data hiding schemes produce the stego-image with high quality after data embedding. In addition, the recovered image is same as the original image after data extracting phase. In conclusion, these two schemes are feasible to offer a safe and efficient approach for multimedia transmission on the Internet. Most of data hiding techniques suffer from the problem of trade-off between payload and image distortion. Finally, in this thesis, a novel data hiding scheme in digital images with the diamond encoding by pixel value adjustment is proposed. The diamond encoding method determines the diamond-shaped size based on the payload of hidden messages. Therefore, the information hiding scheme with diamond encoding can hide a large amount of information in a cover image with little distortion.ABSTRACT in Chinese I ABSTRACT in English III ACKNOWLEDGE V TABLE OF CONTENTS VI LIST OF TABLES VIII LIST OF FIGURES IX 1 INTRODUCTION 1 1.1 OVERVIEW 1 1.2 SURVEY OF LOSSLESS DATA HIDING SCHEMES 5 1.3 ORGANIZATION 7 2 RELATED WORKS 8 2.1 REVERSIBLE WATERMARKING BY DIFFERENCE EXPANSION 8 2.2 REVERSIBLE WATERMARKING BY GENERALIZED DIFFERENCE EXPANSION 10 2.3 THE PIXEL VALUE PREDICTION TECHNIQUE 11 2.4 EXPLOITING MODIFICATION DIRECTION EMBEDDING SCHEME 14 3 ADAPTIVE LOSSLESS STEGANOGRAPHIC SCHEME WITH CENTRALIZED DIFFERENCE EXPANSION 17 3.1 INTRODUCTION 17 3.2 THE PROPOSED SCHEME 18 3.2.1 The Centralized Difference Expansion 19 3.2.2 The Adaptive Embedding Procedure 21 3.2.3 The Adaptive Extraction Procedure 23 3.3 EXPERIMENTS RESULTS AND DISCUSSIONS 28 4 A HIGH CAPACITY REVERSIBLE DATA HIDING SCHEME WITH EDGE PREDICTION AND DIFFERENCE EXPANSION 41 4.1 INTRODUCTION 41 4.2 THE PROPOSED SCHEME 42 4.2.1 The Pixel Value Prediction Phase 43 4.2.2 The Capacity Estimation Phase 43 4.2.3 The Data Embedding Phase 44 4.2.4 The Data Extraction and Original Pixel Recorvery Phase 46 4.3 EXPERIMENTAL RESULTS 48 5 A NOVEL IMAGE DATA HIDING SCHEME WITH DIAMOND ENCODING 59 5.1 INTRODUCTION 59 5.2 THE PROPOSED SCHEME 60 5.2.1 The Diamond Encoding 60 5.2.2 The Framework of Diamond Encoding 63 5.3 EXPERIMENTAL RESULTS 69 6 CONCLUSIONS AND FUTURE WORKS 79 REFERENCES 8