Steganalytic Methods for the Detection of Histogram Shifting Data Hiding Schemes

Peer-reviewedIn this paper, several steganalytic techniques designed to detect the existence of hidden messages using histogram shifting schemes are presented. Firstly, three techniques to identify specific histogram shifting data hiding schemes, based on detectable visible alterations on the histogram or abnormal statistical distributions, are suggested. Afterwards, a general technique capable of detecting all the analyzed histogram shifting data hiding methods is suggested. This technique is based on the effect of histogram shifting methods on the ¿volatility¿ of the histogram of the difference image. The different behavior of volatility whenever new data are hidden makes it possible to identify stego and cover images

Bit Plane Coding Based Steganography Technique for JPEG2000 Images and Videos

In this paper, a Bit Plane Coding (BPC) based steganography technique for JPEG2000 images and Motion JPEG2000 video is proposed. Embedding in this technique is performed in the lowest significant bit planes of the wavelet coefficients of a cover image. In JPEG2000 standard, the number of bit planes of wavelet coefficients to be used in encoding is dependent on the compression rate and are used in Tier-2 process of JPEG2000. In the proposed technique, Tier-1 and Tier-2 processes of JPEG2000 and Motion JPEG2000 are executed twice on the encoder side to collect the information about the lowest bit planes of all code blocks of a cover image, which is utilized in embedding and transmitted to the decoder. After embedding secret data, Optimal Pixel Adjustment Process (OPAP) is applied on stego images to enhance its visual quality. Experimental results show that proposed technique provides large embedding capacity and better visual quality of stego images than existing steganography techniques for JPEG2000 compressed images and videos. Extracted secret image is similar to the original secret image

Paperless Transfer of Medical Images: Storing Patient Data in Medical Images

Medical images have become an integral part ofpatient diagnosis in recent years. With the introduction of HealthInformation Management Systems (HIMS) used for the storageand sharing of patient data, as well as the use of the PictureArchiving and Communication Systems (PACS) formanipulating and storage of CT Scans, X-rays, MRIs and othermedical images, the security of patient data has become a seriousconcern for medical professionals. The secure transfer of theseimages along with patient data is necessary for maintainingconfidentiality as required by the Data Protection Act, 2011 inTrinidad and Tobago and similar legislation worldwide. Tofacilitate this secure transfer, different digital watermarking andsteganography techniques have been proposed to safely hideinformation in these digital images. This paper focuses on theamount of data that can be embedded into typical medical imageswithout compromising visual quality. In addition, ExploitingModification Direction (EMD) is selected as the method of choicefor hiding information in medical images and it is compared tothe commonly used Least Significant Bit (LSB) method.Preliminary results show that by using EMD there little to nodistortion even at the highest embedding capacity

A DATA HIDING SCHEME BASED ON CHAOTIC MAP AND PIXEL PAIRS

Information security is one of the most common areas of study today. In the literature, there are many algorithms developed in the information security. The Least Significant Bit (LSB) method is the most known of these algorithms. LSB method is easy to apply however it is not effective on providing data privacy and robustness. In spite of all its disadvantages, LSB is the most frequently used algorithm in literature due to providing high visual quality. In this study, an effective data hiding scheme alternative to LSB, 2LSBs, 3LSBs and 4LSBs algorithms (known as xLSBs), is proposed. In this method, random numbers which are to be used as indices of pixels of the cover image are obtained from chaotic maps and data hiding process is applied on the values of these pixels by using modulo function. Calculated values are embedded in cover image as hidden data. Success of the proposed data hiding scheme is assessed by Peak Signal-to-Noise Ratio (PSNR), payload capacity and quality

A Reversible Data Hiding Scheme Based on Side Match Vector Quantization

[[abstract]]Many researchers have studied reversible data hiding techniques in recent years and most have proposed reversible data hiding schemes that guarantee only that the original cover image can be reconstructed completely. Once the secret data are embedded in the compression domain and the receiver wants to store the cover image in a compression mode to save storage space, the receiver must extract the secret data, reconstruct the cover image, and compress the cover image again to generate compression codes. In this paper, we present a reversible data hiding scheme based on side match vector quantization (SMVQ) for digitally compressed images. With this scheme, the receiver only performs two steps to achieve the same goal: extract the secret data and reconstruct the original SMVQ compression codes. In terms of the size of the secret data, the visual quality, and the compression rate, experimental results show that the performance of our proposed scheme is better than those of other information hiding schemes for VQ-based and SMVQ-based compressed images. The experimental results further confirm the effectiveness and reversibility of the proposed schem

Vector Quantization Techniques for Approximate Nearest Neighbor Search on Large-Scale Datasets

The technological developments of the last twenty years are leading the world to a new era. The invention of the internet, mobile phones and smart devices are resulting in an exponential increase in data. As the data is growing every day, finding similar patterns or matching samples to a query is no longer a simple task because of its computational costs and storage limitations. Special signal processing techniques are required in order to handle the growth in data, as simply adding more and more computers cannot keep up.Nearest neighbor search, or similarity search, proximity search or near item search is the problem of finding an item that is nearest or most similar to a query according to a distance or similarity measure. When the reference set is very large, or the distance or similarity calculation is complex, performing the nearest neighbor search can be computationally demanding. Considering today’s ever-growing datasets, where the cardinality of samples also keep increasing, a growing interest towards approximate methods has emerged in the research community.Vector Quantization for Approximate Nearest Neighbor Search (VQ for ANN) has proven to be one of the most efficient and successful methods targeting the aforementioned problem. It proposes to compress vectors into binary strings and approximate the distances between vectors using look-up tables. With this approach, the approximation of distances is very fast, while the storage space requirement of the dataset is minimized thanks to the extreme compression levels. The distance approximation performance of VQ for ANN has been shown to be sufficiently well for retrieval and classification tasks demonstrating that VQ for ANN techniques can be a good replacement for exact distance calculation methods.This thesis contributes to VQ for ANN literature by proposing five advanced techniques, which aim to provide fast and efficient approximate nearest neighbor search on very large-scale datasets. The proposed methods can be divided into two groups. The first group consists of two techniques, which propose to introduce subspace clustering to VQ for ANN. These methods are shown to give the state-of-the-art performance according to tests on prevalent large-scale benchmarks. The second group consists of three methods, which propose improvements on residual vector quantization. These methods are also shown to outperform their predecessors. Apart from these, a sixth contribution in this thesis is a demonstration of VQ for ANN in an application of image classification on large-scale datasets. It is shown that a k-NN classifier based on VQ for ANN performs on par with the k-NN classifiers, but requires much less storage space and computations