Security for eHealth system: data hiding in AMBTC compressed images via gradient-based coding

Nowadays since the Internet is ubiquitous, the frequency of data transfer through the public network is increasing. Hiding secure data in these transmitted data has emerged broad security issue, such as authentication and copyright protection. On the other hand, considering the transmission efficiency issue, image transmission usually involves image compression in Internet-based applications. To address both issues, this paper presents a data hiding scheme for the image compression method called absolute moment block truncation coding (AMBTC). First, an image is divided into non-overlapping blocks through AMBTC compression, the blocks are classified four types, namely smooth, semi-smooth, semi-complex, and complex. The secret data are embedded into the smooth blocks by using a simple replacement strategy. The proposed method respectively embeds nine bits (and five bits) of secret data into the bitmap of the semi-smooth blocks (and semi-complex blocks) through the exclusive-or (XOR) operation. The secret data are embedded into the complex blocks by using a hidden function. After the embedding phase, the direct binary search (DBS) method is performed to improve the image quality without damaging the secret data. The experimental results demonstrate that the proposed method yields higher quality and hiding capacity than other reference methods.

Image Compression based on Block Truncation Coding Using Clifford Algebra

Due to advancement in communication technology, data are transmitted over the network which is either confidential or private. So, the information security is one of the most critical factors considered when secret data is transmitted between two parties. Another important issue is the bandwidth utilisation for data transmission. Image steganography is a widely used technique for data hiding. It is used in critical applications like military and medical areas. Most of the work is done in uncompressed images, which leads to high storage and large bandwidth required for transmission. Keeping these two factors in mind, this paper presents the multilevel histogram shifting technique in the compressed domain with the addition of adaptive block division scheme to improve the embedding capacity as well as reduce the utilisation of the bandwidth. In this method, absolute moment block truncation coding (AMBTC) compression technique has been used for compression because of its good compression ratio.

Absolute Moment Block Truncation Coding (AMBTC) is an efficient and fast lossy compression technique for still images. It is easy to implement compared to transform coding. Interpolative AMBTC is a modified method of AMBTC compression. In this paper image data hiding in images using Interpolative AMBTC is proposed. In this method the secret image is stored in the bit plane of the AMBTC compressed image and is recovered during decompression process. Experimental results show that the proposed method achieves good quality decompressed image with low computational complexity.KeywordsData hidingCompressionBlock truncation codingAMBTCBit plane

WITHDRAWN: Multi-classifiers and deep cnns for implementing image block truncation coding in e-learning platform

Data hiding encompasses a wide range of applications related to hiding messages in digital images. The visual redundancy of images makes it possible to embed data in the images without attracting attention. Increasing the hiding capacity and decreasing the hiding distortion are prime objectives that data hiding intends to achieve. In this paper, we propose a high-payload data hiding scheme based on absolute moment block truncation coding (AMBTC) to minimize the impact of hiding. A two-level minimum mean square error (MMSE) quantizer generated by AMBTC is used to decrease the distortion associated with hiding. Also, we present a lookup table based on the symmetric property for adaptively hiding secrets in pixels to achieve high hiding capacity. We can embed almost 1.9 bits per pixel (bpp) with a high image quality of an average of 31 dB. Only 5.3% of pixels are changed during the data-hiding process. Compared with other schemes, we can use 1 bpp more relative payload for embedding with the same stego image quality. The experimental results show that the proposed scheme has better hiding performance because it allows a huge amount of secret data to be hidden while maintaining the high visual quality of the stego image.

This paper describes a color image compression scheme based on absolute moment block truncation coding (AMBTC) using Huffman coding. High degree of correlation between the RGB planes of a color image is reduced by transforming them to Y, Cb, Cr planes. Each plane is then encoded separately using the proposed method. The experiment has been conducted using the proposed scheme on standard color images. Experimental results show that the compression ratio of the proposed method is significantly improved when compared to color BTC and color AMBTC, with a relative distortion of reconstructed images.

Block truncation coding (BTC) is a well known lossy compression scheme. Due to its low complexity and easy implementation, BTC has gained wide interest in its further development and application for image compression. Based on simple thresholding, BTC retains sharp edges and thus leads to artifacts such as the staircase effect. The second problem encountered in BTC is the splitting of homogeneous regions, which produces false contours. In this work a fuzzy approach of BTC to avoid truncating homogeneous blocks and to preserve smooth edges in two-cluster blocks is proposed. Each image block, viewed as a fuzzy set, is segmented into two clusters using a fuzzy clustering algorithm. The block is then encoded by modified fuzzy weighted means of the two clusters. Initialization strategies of the fuzzy clustering algorithm and a contextual quantization method are proposed. Experimental results show an improvement of visual quality of reconstructed images and peak signal-to-noise ratio when compared to BTC, economical BTC (EBTC), absolute moment BTC (AMBTC), and a minimum mean square error quantizer (MMSEQ). Computation time required by AMBTC, EBTC, and fuzzy BTC methods are reported.

Social networking and cloud computing are being extensively used, and in this era, the frequency of sending information or images to each other is increasing. The prevention of private information leakage during communication over the Internet has become a concern in the past decades. Several data protection methods, such as cryptographic, watermarking, and steganography techniques, have been proposed to protect private data. In this paper, an embedding method is proposed based on an absolute moment block truncation coding (AMBTC)-compressed image. High and low mean tables are extracted from a compressed image and are divided into non-overlapping blocks. An adaptive variable N-bit bit plane truncation image embedding method is proposed to embed the secret data in each block. In this method, at the receiver end, the secret data are extracted, and the original AMBTC image could be recovered by recalling the stored peak and zero points. In addition, a chaotic encryption scheme is integrated into the proposed system to improve robustness against security vulnerability. The results show that the proposed method has superior performance and higher payload compared with the reference methods.

In this paper, an optimized data embedding method based on Huang et al.’s work for absolute moment block truncation coding (AMBTC) is proposed. Huang et al.’s work successfully exploits the difference of quantization levels (QLs) for data embedment and has an excellent embedding performance. However, the modified QLs are not adjusted to minimize the distortion. In some rare cases, they might exceed the grayscale range. Moreover, the order of QLs in smooth blocks is not utilized for data embedment, losing the chance to embed one additional bit without deteriorating the image block. We propose a method to give analytical solutions to adjust QLs such that the distortions in both smooth and complex blocks are minimized. A subtle mechanism is also provided to ensure that no QLs will overflow or underflow. Moreover, the order of QLs is utilized in data embedment to further increase the payload without sacrificing the image quality. The experimental results reveal that the proposed method offers a better image quality over Huang et al.’s and other state-of-the-art works while providing a comparable or larger payload.

Mobile edge computing provides low-latency service computing for the Internet of Things (IoT). Considering the computational cost of high-quality image steganography in practical mobile applications, we believe that mobile edge computing could provide real-time service computing for covert communications. As a mainstream approach to convert communication, image steganographic algorithms prefer to hide secret data in well-textured regions in order to reduce the possibility of being detected. Recently, the generative adversarial networks (GAN) has become one of the most popular architectures for image steganography. However, the GAN-based image steganographic algorithms directly conduct the secret data embedding on the entire cover images and do not sufficiently take the regional texture complexity into account, which will compromise the anti-detection ability. To address this issue, we propose a novel image steganographic algorithm on the generated foreground object region with rich textures. More specifically, the foreground object region is generated onto a given cover image by the GAN, and the secret data is embedded in the foreground object region simultaneously during the generation of the region. The experimental results show that the proposed method can resist steganalysis effectively without significant degradation of image quality and achieve real-time processing.

Objective: For reducing the data redundancy to save more hardware space and transmission bandwidth, data compression is used. Methods: For image compression, we have proposed a new method based on c-mean and absolute moment Block Truncation Coding (BTC) techniques. In this technique an image is segmented into several non-overlapping blocks. For each block, calculate the weighted c-mean by means of linear combination of its arithmetic mean and c-mean. The c-mean is evaluated by expressing every pixel into a sum of largest perfect square of non-negative integer. For each image block, the pixels are classified into two range of values based on weighted c-mean. The gray values of the image block which are greater than block weighted c-mean are considered as upper range and otherwise lower range. The weighted c-mean of upper range and lower range are termed as higher c-mean and lower c-mean respectively. Then construct the binary block matrix (0/1) based on weighted c-mean as a quantization level. In the decoder part, an image block is reconstructed by replacing the 1's with upper c-mean and 0's by lower c-mean of block matrix. Findings: The newly designed method is tested some8-bit standard images. It is also compared with related works in term of Peak Signal to Noise Ratio, Bit Rate, and Structural similarity index. It shows the effectiveness of the proposed method. Applications: Image storage is required for several purposes like document, medical images, magnetic resonance imaging and radiology, motion pictures etc. All such applications are based on image compression.

In current years, Internet of Things (IoT) has come a long way and is well emerged within many organizations and fields covering the sector of healthcare. Continuously, encompassing of the Internet of Medical Things (IoMT) in the field of the medical sector helps in the rise of medical data and analysis of that. Smart healthcare plays important role in human life and various research projects are running on it. It consists of diverse ingredients like wireless communication technology, cloud computing, edge computing, medical sensors, artificial intelligence (AI), IoMT, and IoT. The chapter introduces IoMT with respect to the multiple challenges like privacy, security and low-power operation faced while incorporating IoT with healthcare sector. For emergency treatment, a cloud-based healthcare framework provides a platform to support patients over internet communication by medical experts. Privacy and security to the medical records are on higher priority due to the very sensitive nature of the content. Moreover, a comprehensive survey on IoMT-based cloud and edge-intelligent healthcare system is also described. The current advancement in the field of IoT, IoMT, cloud computing, and AI related to privacy and security, some pathways for forthcoming work in collaboration of healthcare sector and IoT based on a collection of challenges and open issues are also addressed.

Secret sharing based on Absolute Moment Block Truncation Coding (AMBTC) has been widely studied. However, the management of stego images is inconvenient as they seem indistinguishable. Moreover, there exists a problem of pixel expansion, which requires more storage space and higher transmission bandwidth. To conveniently manage the stego images, we use multiple cover images to make the stego images seem to be visually different from with each other. Futhermore, the stego images are different, which will not cause the attacker's suspicion and increase the security of the scheme. And traditional Visual Secret Sharing (VSS) is fused to eliminate pixel expansion. After images are compressed by AMBTC algorithm, the quantization levels and the bitmap corresponding to each block are obtained. At the same time, when the threshold is (k,k), bitmaps can be recovered losslessly, and the slight degradation of image quality is only caused by the compression itself. When the threshold is another value, the recovered image and the cover images can be recovered with satisfactory image quality. The experimental results and analyses show the effectiveness and advantages of our scheme.

Low Complexity Image Compression Algorithm for Wireless Channel